Cluster ensembles: a survey of approaches with recent extensions and applications. (English) Zbl 1387.68195
Summary: Cluster ensembles have been shown to be better than any standard clustering algorithm at improving accuracy and robustness across different data collections. This meta-learning formalism also helps users to overcome the dilemma of selecting an appropriate technique and the corresponding parameters, given a set of data to be investigated. Almost two decades after the first publication of a kind, the method has proven effective for many problem domains, especially microarray data analysis and its down-streaming applications. Recently, it has been greatly extended both in terms of theoretical modelling and deployment to problem solving. The survey attempts to match this emerging attention with the provision of fundamental basis and theoretical details of state-of-the-art methods found in the present literature. It yields the ranges of ensemble generation strategies, summarization and representation of ensemble members, as well as the topic of consensus clustering. This review also includes different applications and extensions of cluster ensemble, with several research issues and challenges being highlighted.
MSC:
| 68T05 | Learning and adaptive systems in artificial intelligence |
| 62H30 | Classification and discrimination; cluster analysis (statistical aspects) |
| 68T20 | Problem solving in the context of artificial intelligence (heuristics, search strategies, etc.) |
| 68-02 | Research exposition (monographs, survey articles) pertaining to computer science |
References:
| [1] | Jiang, D.; Tang, C.; Zhang, A., Cluster analysis for gene expression data: A survey, IEEE Trans. Knowl. Data Eng., 16, 1370-1386 (2004) |
| [2] | R.C. Wu, R.S. Chen, C.C. Chang, J.Y. Chen, Data mining application in customer relationship management of credit card business, in: Proceedings of international conference on Computer software and applications, 2005, pp. 39-40.; R.C. Wu, R.S. Chen, C.C. Chang, J.Y. Chen, Data mining application in customer relationship management of credit card business, in: Proceedings of international conference on Computer software and applications, 2005, pp. 39-40. |
| [3] | Bhatia, S. K.; Deogun, J. S., Conceptual clustering in information retrieval, IEEE Trans. Syst. Man Cybern., 28, 427-436 (1998) |
| [4] | Zhang, J.; Mostafa, J.; Tripathy, H., Information retrieval by semantic analysis and visualisation of the concept space of D-Lib magazine, D-Lib Mag., 8 (2002) |
| [5] | Costa, J. A.F.; de Andrade Netto, M., Cluster analysis using self-organising maps and image processing techniques, Proc. IEEE Int. Conf. Syst. Man Cybern., 5, 367-372 (1999) |
| [6] | H. Tao, T.S. Huang, Color image edge detection using cluster analysis, in: Proceedings of IEEE International Conference on Image Processing, 1997, pp. 834-836.; H. Tao, T.S. Huang, Color image edge detection using cluster analysis, in: Proceedings of IEEE International Conference on Image Processing, 1997, pp. 834-836. |
| [7] | Day, G. S.; Heeler, R. M., Using cluster analysis to improve marketing experiments, J. Market. Res., 8, 340-347 (1971) |
| [8] | Sheppard, A. G., The sequence of factor analysis and cluster analysis: Differences in segmentation and dimensionality through the use of raw and factor scores, Tourism Anal., 1, 49-57 (1996) |
| [9] | Henry, D. B.; Tolan, P. H.; Gorman-Smith, D., Cluster analysis in family psychology research, J. Family Psychol., 19, 121-132 (2005) |
| [10] | Kim, K.; Ahn, H., A recommender system using GA K-means clustering in an online shopping market, Expert Syst. Appl., 34, 1200-1209 (2008) |
| [11] | Bredel, M.; Bredel, C.; Juric, D.; Harsh, G.; Vogel, H.; Recht, L.; Sikic, B., Functional network analysis reveals extended gliomagenesis pathway maps and three novel MYC-interacting genes in human gliomas, Cancer Res., 65, 8679-8689 (2005) |
| [12] | Kim, E.; Kim, S.; Ashlock, D.; Nam, D., MULTI-K: Accurate classification of microarray subtypes using ensemble k-means clustering, BMC Bioinform., 10, 260 (2009) |
| [13] | Sorlie, T.; Tibshirani, R.; Parker, J.; Hastie, T.; Marron, J.; Nobel, A.; Deng, S.; Johnsen, H.; Pesich, R.; Geisler, S., Repeated observation of breast tumor subtypes in independent gene expression data sets, Proc. Natl. Acad. Sci. USA, 100, 8418-8423 (2003) |
| [14] | Jain, A. K.; Murty, M. N.; Flynn, P. J., Data clustering: A review, ACM Comput. Survey, 31, 264-323 (1999) |
| [15] | Ahmad, A.; Dey, L., A k-mean clustering algorithm for mixed numeric and categorical data, Data Knowl. Eng., 63, 503-527 (2007) |
| [16] | Z. Huang, Claustering large data sets with mixed numeric and categorical values, in: Proceedings of the First Pacific Asia Knowledge Discovery and Data Mining Conference, 1997, pp. 21-34.; Z. Huang, Claustering large data sets with mixed numeric and categorical values, in: Proceedings of the First Pacific Asia Knowledge Discovery and Data Mining Conference, 1997, pp. 21-34. |
| [17] | Dudoit, S.; Fridyand, J., A prediction-based resampling method for estimating the number of clusters in a dataset, Genome Biol., 3 (2002), RESEARCH0036 |
| [18] | Boongoen, T.; Shen, Q., Nearest-neighbour guided evaluation of data reliability and its applications, IEEE Trans. Syst. Man Cybern. B, 40, 1622-1633 (2010) |
| [19] | Rand, W. M., Objective criteria for the evaluation of clustering methods, J. Amer. Statist. Assoc., 66, 846-850 (1971) |
| [20] | Iam-On, N.; Boongoen, T.; Garrett, S., LCE: A link-based cluster ensemble method for improved gene expression data analysis, Bioinformatics, 26, 1513-1519 (2010) |
| [21] | Duda, R. O.; Hart, P. E.; Stork, D. G., Pattern Classification (2000), Wiley-Interscience |
| [22] | Fred, A. L.N.; Jain, A. K., Combining multiple clusterings using evidence accumulation, IEEE Trans. Pattern Anal. Mach. Intell., 27, 835-850 (2005) |
| [23] | Xue, H.; Chen, S.; Yang, Q., Discriminatively regularized least-squares classification, Pattern Recognit., 42, 93-104 (2009) · Zbl 1159.68547 |
| [24] | Topchy, A. P.; Jain, A. K.; Punch, W. F., Clustering ensembles: Models of consensus and weak partitions, IEEE Trans. Pattern Anal. Mach. Intell., 27, 1866-1881 (2005) |
| [25] | Jain, A. K.; Duin, R.; Mao, J., Statistical pattern recognition: A review, IEEE Trans. Pattern Anal. Mach. Intell., 22, 4-37 (2000) |
| [26] | J. McQueen, Some methods for classification and analysis of multivariate observations, in: Proceedings of the Fifth Berkeley Symposium on Mathematical Statistics and Probability, 1967, pp. 281-297.; J. McQueen, Some methods for classification and analysis of multivariate observations, in: Proceedings of the Fifth Berkeley Symposium on Mathematical Statistics and Probability, 1967, pp. 281-297. · Zbl 0214.46201 |
| [27] | Z. Huang, A bfast clustering algorithm to cluster very large categorical data sets in data mining, in: Proceedings of the SIGMOD Workshop on Research Issues on Data Mining and Knowledge Discovery, 1997, pp. 1-8.; Z. Huang, A bfast clustering algorithm to cluster very large categorical data sets in data mining, in: Proceedings of the SIGMOD Workshop on Research Issues on Data Mining and Knowledge Discovery, 1997, pp. 1-8. |
| [28] | Tan, P. N.; Steinbach, M.; Kumar, V., Introduction to Data Mining (2005), Addison Wesley |
| [29] | M. Ester, H.P. Kriegel, J. Sander, X. Xu, A density-based algorithm for discovering clusters in large spatial databases with noise, in: Proceedings of International Conference on Knowledge Discovery and Data Mining, 1996, pp. 226-231.; M. Ester, H.P. Kriegel, J. Sander, X. Xu, A density-based algorithm for discovering clusters in large spatial databases with noise, in: Proceedings of International Conference on Knowledge Discovery and Data Mining, 1996, pp. 226-231. |
| [30] | Kohonen, T., Self-organized formation of topologically correct feature maps, Biol. Cybernet., 43, 59-69 (1982) · Zbl 0466.92002 |
| [31] | Flexer, A., On the use of self-organizing maps for clustering and visualization, Intell. Data Anal., 5, 373-384 (2001) · Zbl 1088.68713 |
| [32] | Vesanto, J.; Alhoniemi, E., Clustering of the self-origanising map, IEEE Trans. Neural Netw., 11, 586-600 (2000) |
| [33] | Furukawa, T., Som of soms, Neural Netw., 22, 463-478 (2009) |
| [34] | Tokunaga, K.; Furukawa, T., Modular network som, Neural Netw., 22, 82-90 (2009) |
| [35] | Wolpert, D. H.; Macready, W. G., No free lunch theorems for search, (Technical Report SFI-TR-95-02-010 (1995), Santa Fe Institute) |
| [36] | L.I. Kuncheva, S.T. Hadjitodorov, Using diversity in cluster ensembles, in: Proceedings of the IEEE International Conference on Systems, Man and Cybernetics, 2004, pp. 1214-1219.; L.I. Kuncheva, S.T. Hadjitodorov, Using diversity in cluster ensembles, in: Proceedings of the IEEE International Conference on Systems, Man and Cybernetics, 2004, pp. 1214-1219. |
| [37] | C. Boulis, M. Ostendorf, Combining multiple clustering systems, in: Proceedings of European Conference on Principles and Practice of Knowledge Discovery in Databases, 2004, pp. 63-74.; C. Boulis, M. Ostendorf, Combining multiple clustering systems, in: Proceedings of European Conference on Principles and Practice of Knowledge Discovery in Databases, 2004, pp. 63-74. |
| [38] | Cristofor, D.; Simovici, D., Finding median partitions using information-theoretical-based genetic algorithms, J. Univ. Comput. Sci., 8, 153-172 (2002) · Zbl 1258.68048 |
| [39] | N. Nguyen, R. Caruana, Consensus clusterings, in: Proceedings of IEEE International Conference on Data Mining, 2007, pp. 607-612.; N. Nguyen, R. Caruana, Consensus clusterings, in: Proceedings of IEEE International Conference on Data Mining, 2007, pp. 607-612. |
| [40] | A.P. Topchy, A.K. Jain, W.F. Punch, A mixture model for clustering ensembles, in: Proceedings of SIAM International Conference on Data Mining, 2004, pp. 379-390.; A.P. Topchy, A.K. Jain, W.F. Punch, A mixture model for clustering ensembles, in: Proceedings of SIAM International Conference on Data Mining, 2004, pp. 379-390. |
| [41] | Fischer, B.; Buhmann, J. M., Bagging for path-based clustering, IEEE Trans. Pattern Anal. Mach. Intell., 25, 1411-1415 (2003) |
| [42] | Gionis, A.; Mannila, H.; Tsaparas, P., Clustering aggregation, ACM Trans. Knowl. Discov. Data, 1, 4-ex (2007) |
| [43] | Domeniconi, C.; Al-Razgan, M., Weighted cluster ensembles: Methods and analysis, ACM Trans. Knowl. Discov. Data, 2, 1-40 (2009) |
| [44] | X.Z. Fern, C.E. Brodley, Solving cluster ensemble problems by bipartite graph partitioning, in: Proceedings of International Conference on Machine Learning, 2004, pp. 36-43.; X.Z. Fern, C.E. Brodley, Solving cluster ensemble problems by bipartite graph partitioning, in: Proceedings of International Conference on Machine Learning, 2004, pp. 36-43. |
| [45] | Strehl, A.; Ghosh, J., Cluster ensembles: A knowledge reuse framework for combining multiple partitions, J. Mach. Learn. Res., 3, 583-617 (2002) · Zbl 1084.68759 |
| [46] | H. Ayad, M. Kamel, Finding natural clusters using multiclusterer combiner based on shared nearest neighbors, in: Proceedings of International Workshop on Multiple Classifier Systems, 2003, pp. 166-175.; H. Ayad, M. Kamel, Finding natural clusters using multiclusterer combiner based on shared nearest neighbors, in: Proceedings of International Workshop on Multiple Classifier Systems, 2003, pp. 166-175. · Zbl 1040.68586 |
| [47] | X.Z. Fern, C.E. Brodley, Random projection for high dimensional data clustering: A cluster ensemble approach, in: Proceedings of International Conference on Machine Learning, 2003, pp. 186-193.; X.Z. Fern, C.E. Brodley, Random projection for high dimensional data clustering: A cluster ensemble approach, in: Proceedings of International Conference on Machine Learning, 2003, pp. 186-193. |
| [48] | A.L.N. Fred, Finding consistent clusters in data partitions, in: Multiple Classifier Systems, 2001, pp. 309-318.; A.L.N. Fred, Finding consistent clusters in data partitions, in: Multiple Classifier Systems, 2001, pp. 309-318. · Zbl 0980.68682 |
| [49] | A.L.N. Fred, A.K. Jain, Data clustering using evidence accumulation, in: Proceedings of International Conference on Pattern Recognition, pp. 276-280.; A.L.N. Fred, A.K. Jain, Data clustering using evidence accumulation, in: Proceedings of International Conference on Pattern Recognition, pp. 276-280. |
| [50] | A.L.N. Fred, A.K. Jain, Robust data clustering, in: Proceedings of IEEE International Conference on Computer Vision and Pattern Recognition, 2003, pp. 128-136.; A.L.N. Fred, A.K. Jain, Robust data clustering, in: Proceedings of IEEE International Conference on Computer Vision and Pattern Recognition, 2003, pp. 128-136. |
| [51] | Monti, S.; Tamayo, P.; Mesirov, J. P.; Golub, T. R., Consensus clustering: A resampling-based method for class discovery and visualization of gene expression microarray data, Mach. Learn., 52, 91-118 (2003) · Zbl 1039.68103 |
| [52] | Kittler, J.; Hatef, M.; Duin, R.; Matas, J., On combining classifiers, IEEE Trans. Pattern Anal. Mach. Intell., 20, 226-239 (1998) |
| [53] | Kuncheva, L. I.; Vetrov, D., Evaluation of stability of k-means cluster ensembles with respect to random initialization, IEEE Trans. Pattern Anal. Mach. Intell., 28, 1798-1808 (2006) |
| [54] | N. Iam-On, T. Boongoen, S. Garrett, Refining pairwise similarity matrix for cluster ensemble problem with cluster relations, in: Proceedings of Eleventh International Conference on Discovery Science, 2008, pp. 222-233.; N. Iam-On, T. Boongoen, S. Garrett, Refining pairwise similarity matrix for cluster ensemble problem with cluster relations, in: Proceedings of Eleventh International Conference on Discovery Science, 2008, pp. 222-233. |
| [55] | Kaufman, L.; Rousseeuw, P. J., Finding Groups in Data: An Introduction to Cluster Analysis (1990), Wiely Publishers: Wiely Publishers New York · Zbl 1345.62009 |
| [56] | Hadjitodorov, S. T.; Kuncheva, L. I.; Todorova, L. P., Moderate diversity for better cluster ensembles, Inform. Fusion, 7, 264-275 (2006) |
| [57] | A.P. Topchy, A.K. Jain, W.F. Punch, Combining multiple weak clusterings, in: Proceedings of IEEE International Conference on Data Mining, 2003, pp. 331-338.; A.P. Topchy, A.K. Jain, W.F. Punch, Combining multiple weak clusterings, in: Proceedings of IEEE International Conference on Data Mining, 2003, pp. 331-338. |
| [58] | Yu, Z.; Wong, H.-S.; Wang, H., Graph-based consensus clustering for class discovery from gene expression data, Bioinformatics, 23, 2888-2896 (2007) |
| [59] | Dudoit, S.; Fridyand, J., Bagging to improve the accuracy of a clustering procedure, Bioinformatics, 19, 1090-1099 (2003) |
| [60] | B. Minaei-Bidgoli, A. Topchy, W. Punch, A comparison of resampling methods for clustering ensembles, in: Proceedings of the International Conference on Machine Learning: Models, Technologies and Applications, 2004, pp. 939-945.; B. Minaei-Bidgoli, A. Topchy, W. Punch, A comparison of resampling methods for clustering ensembles, in: Proceedings of the International Conference on Machine Learning: Models, Technologies and Applications, 2004, pp. 939-945. |
| [61] | E. Bingham, H. Mannila, Random projection in dimensionality reduction: Applications to image and text data, in: Proceedings of International Conference on Knowledge Discovery and Data Mining, 2001, pp. 245-250.; E. Bingham, H. Mannila, Random projection in dimensionality reduction: Applications to image and text data, in: Proceedings of International Conference on Knowledge Discovery and Data Mining, 2001, pp. 245-250. |
| [62] | Avogadri, R.; Valentini, G., Fuzzy ensemble clustering based on random projections for DNA microarray data analysis, Artif. Intell. Med., 45, 173-183 (2009) |
| [63] | Breiman, L., Bagging predictors, Mach. Learn., 24, 123-140 (1996) · Zbl 0858.68080 |
| [64] | S. Kim, J. Lee, Ensemble clustering method based on the resampling similarity measure for gene expression data, in: Statistical Methods in Medical Research, 2007, pp. 1-26.; S. Kim, J. Lee, Ensemble clustering method based on the resampling similarity measure for gene expression data, in: Statistical Methods in Medical Research, 2007, pp. 1-26. |
| [65] | X. Hu, I. Yoo, Cluster ensemble and its applications in gene expression analysis, in: Proceedings of Asia-Pacific Bioinformatics Conference, 2004, pp. 297-302.; X. Hu, I. Yoo, Cluster ensemble and its applications in gene expression analysis, in: Proceedings of Asia-Pacific Bioinformatics Conference, 2004, pp. 297-302. |
| [66] | M. Law, A. Topchy, A.K. Jain, Multiobjective data clustering, in: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, 2004 pp. 424-430.; M. Law, A. Topchy, A.K. Jain, Multiobjective data clustering, in: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, 2004 pp. 424-430. |
| [67] | A.L.N. Fred, A.K. Jain, Learning pairwise similarity for data clustering. in: Proceedings of International Conference on Pattern Recognition, 2006, pp. 925-928.; A.L.N. Fred, A.K. Jain, Learning pairwise similarity for data clustering. in: Proceedings of International Conference on Pattern Recognition, 2006, pp. 925-928. |
| [68] | Bauer, E.; Kohavi, R., An empirical comparison of voting classification algortihms: Bagging, boosting, and variants, Mach. Learn., 36, 105-139 (1999) |
| [69] | Lam, L.; Suen, C. Y., Application of majority voting to pattern recognition: An analysis of its behavior and performance, IEEE Trans. Syst. Man Cybern., 22, 553-568 (1997) |
| [70] | A.P. Topchy, M.H.C. Law, A.K. Jain, A.L. Fred, Analysis of consensus partition in cluster ensemble, in: Proceedings of IEEE International Conference on Data Mining, 2004, pp. 225-232.; A.P. Topchy, M.H.C. Law, A.K. Jain, A.L. Fred, Analysis of consensus partition in cluster ensemble, in: Proceedings of IEEE International Conference on Data Mining, 2004, pp. 225-232. |
| [71] | Munkres, J., Algorithms for the assignment and transportation problems, J. SIAM, 5, 32-38 (1957) · Zbl 0083.15302 |
| [72] | H. Ayad, M. Kamel, Cluster-based cumulative ensembles, in: Proceedings of International Workshop on Multiple Classifier Systems, 2005, pp. 236-245.; H. Ayad, M. Kamel, Cluster-based cumulative ensembles, in: Proceedings of International Workshop on Multiple Classifier Systems, 2005, pp. 236-245. |
| [73] | E. Dimitriadou, A. Weingessel, K. Hornik, Voting-merging: An ensemble method for clustering, in: Proceedings of International Conference on Artificial Neural Networks, 2001, pp. 217-224.; E. Dimitriadou, A. Weingessel, K. Hornik, Voting-merging: An ensemble method for clustering, in: Proceedings of International Conference on Artificial Neural Networks, 2001, pp. 217-224. · Zbl 1005.68932 |
| [74] | Dimitriadou, E.; Weingessel, A.; Hornik, K., A combination scheme for fuzzy clustering, Int. J. Pattern Recognit. Artif. Intell., 16, 901-912 (2002) |
| [75] | D. Frossyniotis, M. Pertselakis, A. Stafylopatis, A multi-clustering fusion algorithm, in: Proceedings of Hellenic Conference on AI, 2002, pp. 225-236.; D. Frossyniotis, M. Pertselakis, A. Stafylopatis, A multi-clustering fusion algorithm, in: Proceedings of Hellenic Conference on AI, 2002, pp. 225-236. · Zbl 1065.68601 |
| [76] | Ayad, H.; Kamel, M., Cumulative voting consensus method for partitions with a variable number of clusters, IEEE Trans. Pattern Anal. Mach. Intell., 30, 160-173 (2008) |
| [77] | Ayad, H.; Kamel, M., On voting-based consensus of cluster ensembles, Pattern Recognit., 43, 1943-1953 (2010) · Zbl 1191.68552 |
| [78] | H. Luo, F. Kong, Y. Li, Combining multiple clusterings via k-modes algorithm, in: Proceedings of International Conference on Advanced Data Mining and Applications, 2006, pp. 308-315.; H. Luo, F. Kong, Y. Li, Combining multiple clusterings via k-modes algorithm, in: Proceedings of International Conference on Advanced Data Mining and Applications, 2006, pp. 308-315. |
| [79] | Bansal, N.; Blum, A.; Chawla, S., Correlation clustering, Mach. Learn., 56, 89-113 (2004) · Zbl 1089.68085 |
| [80] | Hochbaum, D. S.; Shmoys, D. B., A best possible heuristic for the k-center problem, Math. Oper. Res., 10, 180-184 (1985) · Zbl 0565.90015 |
| [81] | Fisher, D. H., Knowledge acquisition via incremental conceptual clustering, Mach. Learn., 2, 139-172 (1987) |
| [82] | Mirkin, B., Reinterpreting the category utility function, Mach. Learn., 45, 219-228 (2001) · Zbl 1007.68155 |
| [83] | P.S. Bradley, U.M. Fayyad, Refining initial points for k-means clustering, in: Proceedings of the International Conference on Machine Learning, 1998, pp. 91-99.; P.S. Bradley, U.M. Fayyad, Refining initial points for k-means clustering, in: Proceedings of the International Conference on Machine Learning, 1998, pp. 91-99. |
| [84] | Swift, S.; Tucker, A.; Vinciotti, V.; Martin, N.; Orengo, C.; Liu, X.; Kellam, P., Consensus clustering and functional interpretation of gene-expression data, Genome Biol., 5, R94 (2004) |
| [85] | F. Weng, Q. Jiang, L. Chen, Z. Hong, Clustering ensemble based on the fuzzy KNN algorithm, in: Proceedings of International Conference on Software Engineering, Artificial Intelligence, Networking, and Parallel/Distributed Computing, 2007, pp. 1001-1006.; F. Weng, Q. Jiang, L. Chen, Z. Hong, Clustering ensemble based on the fuzzy KNN algorithm, in: Proceedings of International Conference on Software Engineering, Artificial Intelligence, Networking, and Parallel/Distributed Computing, 2007, pp. 1001-1006. |
| [86] | Y. Li, J. Yu, P. Hao, Z. Li, Clustering ensembles based on normalized edges, in: Proceedings of Pacific-Asia Conference on Advances in Knowledge Discovery and Data Mining, 2007, pp. 664-671.; Y. Li, J. Yu, P. Hao, Z. Li, Clustering ensembles based on normalized edges, in: Proceedings of Pacific-Asia Conference on Advances in Knowledge Discovery and Data Mining, 2007, pp. 664-671. |
| [87] | Guha, S.; Rastogi, R.; Shim, K., ROCK: A robust clustering algorithm for categorical attributes, Inf. Syst., 25, 345-366 (2000) |
| [88] | Bezdek, J., Pattern Recognition with Fuzzy Objective Function Algorithms (1981), Plenum Press: Plenum Press New York · Zbl 0503.68069 |
| [89] | Bezdek, J.; Hathaway, R., Recent convergence results for the fuzzy c-means clustering algorithms, J. Classification, 5, 237-247 (1988) |
| [90] | Shi, J.; Malik, J., Normalized cuts and image segmentation, IEEE Trans. Pattern Anal. Mach. Intell., 22, 888-905 (2000) |
| [91] | Karypis, G.; Kumar, V., A fast and high quality multilevel scheme for partitioning irregular graphs, SIAM J. Sci. Comput., 20, 359-392 (1998) · Zbl 0915.68129 |
| [92] | Karypis, G.; Kumar, V., Multilevel k-way partitioning scheme for irregular graphs, J. Parallel Distrib. Comput., 48, 96-129 (1998) |
| [93] | Karypis, G.; Kumar, V., A parallel algorithm for multilevel graph-partitioning and sparse matrix ordering, J. Parallel Distrib. Comput., 48, 71-95 (1998) |
| [94] | Karypis, G.; Aggarwal, R.; Kumar, V.; Shekhar, S., Multilevel hypergraph partitioning: Applications in vlsi domain, IEEE Trans. VLSI Syst., 7, 69-79 (1999) |
| [95] | Ng, A.; Jordan, M.; Weiss, Y., On spectral clustering: Analysis and an algorithm, Adv. Neural Inf. Process. Syst., 14, 849-856 (2001) |
| [96] | Domeniconi, C.; Gunopulos, D.; Yan, B.; Al-Razgan, M.; Papadopoulos, D., Locally adaptive metrics for clustering high-dimensional data, Data Mining Knowl. Discov., 14, 63-97 (2007) |
| [97] | Reuther, P.; Walter, B., Survey on test collections and techniques for personal name matching, Int. J. Metadata Semant. Ontol., 1, 89-99 (2006) |
| [98] | G. Jeh, J. Widom, SimRank: A measure of structural-context similarity, in: Proceedings of International Conference on Knowledge Discovery and Data Mining, 2002, pp. 538-543.; G. Jeh, J. Widom, SimRank: A measure of structural-context similarity, in: Proceedings of International Conference on Knowledge Discovery and Data Mining, 2002, pp. 538-543. |
| [99] | Fern, X. Z.; Lin, W., Cluster ensemble selection, Statist. Anal. Data Mining, 1, 128-141 (2008) |
| [100] | Alizadeh, H.; Parvin, H.; Parvin, S., A framework for cluster ensemble based on a max metric as cluster evaluator, IAENG Int. J. Comput. Sci., 39, 10-19 (2012) |
| [101] | Zhang, S.; Wong, H. S.; Shen, Y., Generalized adjusted rand indices for cluster ensembles, Pattern Recognit., 45, 2214-2226 (2012) · Zbl 1234.68358 |
| [102] | Parvin, H.; Minaei-Bidgoli, B., A clustering ensemble framework based on elite selection of weighted clusters, Adv. Data Anal. Classif., 7, 181-208 (2013) · Zbl 1267.62076 |
| [103] | Alizadeh, H.; Minaei-Bidgoli, B.; Parvin, H., Cluster ensemble selection based on a new cluster stability measure, Intell. Data Anal., 18, 389-408 (2014) |
| [104] | Alizadeh, H.; Minaei-Bidgoli, B.; Parvin, H., To improve the quality of cluster ensembles by selecting a subset of base clusters, J. Experiment. Theoret. Artif. Intell., 26, 127-150 (2014) |
| [105] | Naldi, M.; Carvalho, A.; Campello, R., Cluster ensemble selection based on relative validity indexes, Data Mining Knowl. Discov., 27, 259-289 (2013) · Zbl 1284.68503 |
| [106] | P. Rastin, R. Kanawati, A multiplex-network based approach for clustering ensemble selection, in: Proceedings of IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining, 2015, pp. 1332-1339.; P. Rastin, R. Kanawati, A multiplex-network based approach for clustering ensemble selection, in: Proceedings of IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining, 2015, pp. 1332-1339. |
| [107] | Alizadeh, H.; Yousefnezhad, M.; Bidgoli, B. M., Wisdom of crowds cluster ensemble, Intell. Data Anal., 19, 485-503 (2015) |
| [108] | J. Jia, X. Xiao, B. Liu, Similarity-based spectral clustering ensemble selection, in: Proceedings of International Conference on Fuzzy Systems and Knowledge Discovery, 2012, pp. 1071-1074.; J. Jia, X. Xiao, B. Liu, Similarity-based spectral clustering ensemble selection, in: Proceedings of International Conference on Fuzzy Systems and Knowledge Discovery, 2012, pp. 1071-1074. |
| [109] | Akbari, E.; Dahlana, H. M.; Ibrahima, R.; Alizadeh, H., Hierarchical cluster ensemble selection, Eng. Appl. Artif. Intell., 39, 146-156 (2015) |
| [110] | Parvin, H.; Minaei-Bidgoli, B., A clustering ensemble framework based on selection of fuzzy weighted clusters in a locally adaptive clustering algorithm, Pattern Anal. Appl., 18, 87-112 (2015) · Zbl 1428.62292 |
| [111] | Yu, Z.; Chen, H.; You, J.; Liu, J.; Wong, H. S.; Han, G.; Li, L., Adaptive fuzzy consensus clustering framework for clustering analysis of cancer data, IEEE/ACM Trans. Comput. Biol. Bioinform., 12, 887-901 (2015) |
| [112] | Lingras, P.; Haider, F., Partially ordered rough ensemble clustering for multi granular representations, Intell. Data Anal., 19, 103-116 (2015) |
| [113] | Hu, J.; Li, T.; Wang, H.; Fujita, H., Hierarchical cluster ensemble model based on knowledge granulation, Knowl.-Based Syst., 91, 179-188 (2016) |
| [114] | Wu, J.; Liu, H.; Xiong, H.; Cao, J.; Chen, J., K-means-based consensus clustering: A unified view, IEEE Trans. Knowl. Data Eng., 27, 155-169 (2014) |
| [115] | Zhang, H.; Yang, Z.; Oja, E., Improving cluster analysis by co-initializations, Pattern Recognit. Lett., 45, 71-77 (2014) |
| [116] | Jia, J.; Xiao, X.; Liu, B.; Jiao, L., Bagging-based spectral clustering ensemble selection, Pattern Recognit. Lett., 32, 1456-1467 (2011) |
| [117] | Parvin, H.; Minaei-Bidgoli, B.; Alinejad-Rokny, H.; Punch, W. F., Data weighing mechanisms for clustering ensembles, Comput. Electr. Eng., 39, 1433-1450 (2013) |
| [118] | Minaei-Bidgoli, B.; Parvin, H.; Alinejad-Rokny, H.; Alizadeh, H.; Punch, W. F., Effects of resampling method and adaptation on clustering ensemble efficacy, Artif. Intell. Rev., 41, 27-48 (2014) |
| [119] | Ren, Y. Z.; Demeniconi, C.; Zhang, G.; Yu, G. X., Weigted object ensemble clustering: Methods and analysis, Knowl. Inform. Syst., 51, 661-689 (2017) |
| [120] | Yang, F.; Li, X.; Li, Q.; Li, T., Exploring the diversity in cluster ensemble generation: Random sampling and random projection, Expert Syst. Appl., 41, 4844-4866 (2014) |
| [121] | Yang, Y.; Jiang, J., Hybrid sampling-based clustering ensemble with global and local constitutions, IEEE Trans. Neural Netw. Learn. Syst., 27, 952-965 (2015) |
| [122] | Yu, Z.; Li, L.; Gao, Y.; You, J.; Liu, J.; Wong, H. S.; Han, G., Hybrid clustering solution selection strategy, Pattern Recognit., 47, 3362-3375 (2014) |
| [123] | Yu, Z.; Wong, H. S.; You, J.; Yu, G.; Han, G., Hybrid cluster ensemble framework based on the random combination of data transformation operators, Pattern Recognit., 45, 1826-1837 (2012) · Zbl 1233.68198 |
| [124] | Yu, Z.; Li, L.; Liu, J.; Zhang, J.; Han, G., Adaptive noise immune cluster ensemble using affinity propagation, IEEE Trans. Knowl. Data Eng., 27, 3176-3189 (2015) |
| [125] | X. Wang, D. Han, C. Han, Rough set based cluster ensemble selection, in: Proceedings of International Conference on Information Fusion, 2013, pp. 438-444.; X. Wang, D. Han, C. Han, Rough set based cluster ensemble selection, in: Proceedings of International Conference on Information Fusion, 2013, pp. 438-444. |
| [126] | Fodeh, S. J.; Brandt, C.; Luong, T. B.; Haddad, A.; Schultz, M.; Murphy, T.; Krauthammer, M., Complementary ensemble clustering of biomedical data, J. Biomed. Inform., 46, 436-443 (2013) |
| [127] | Alizadeth, H.; Minaei-Bidgoli, B.; Parvin, H., Optimizing fuzzy cluster ensemble in string representation, Int. J. Pattern Recognit. Artif. Intell., 27, 1-22 (2013) |
| [128] | Wang, T., Ca-tree: A hierarchical structure for efficient and scalable coassociation-based cluster ensembles, IEEE Trans. Syst. Man Cybern. B, 41, 1083-4419 (2011) |
| [129] | L. Du, X. Li, Y.D. Shen, Cluster ensembles via weighted graph regularized nonnegative matrix factorization, in: Proceedings of International Conference on Advanced Data Mining and Applications, 2011, pp. 215-228.; L. Du, X. Li, Y.D. Shen, Cluster ensembles via weighted graph regularized nonnegative matrix factorization, in: Proceedings of International Conference on Advanced Data Mining and Applications, 2011, pp. 215-228. |
| [130] | Huang, D.; Lai, J.; Wang, C. D., Ensemble clustering using factor graph, Pattern Recognit., 50, 131-142 (2016) · Zbl 1395.62157 |
| [131] | Wu, O.; Hu, W.; Maybank, S. J.; Zhu, M.; Li, B., Efficient clustering aggregation based on data fragments, IEEE Trans. Syst. Man Cybern. B, 40, 913-926 (2012) |
| [132] | Chung, C. H.; Dai, B. R., A fragment-based iterative consensus clustering algorithm with a robust similarity, Knowl. Inform. Syst., 41, 591-609 (2014) |
| [133] | C. Wang, Z. She, L. Cao, Coupled clustering ensemble: Incorporating coupling relationships both between base clusterings and objects, in: Proceedings of IEEE International Conference on Data Engineering, 2013, pp. 374-385.; C. Wang, Z. She, L. Cao, Coupled clustering ensemble: Incorporating coupling relationships both between base clusterings and objects, in: Proceedings of IEEE International Conference on Data Engineering, 2013, pp. 374-385. |
| [134] | Iam-On, N.; Boongoen, T.; Garrett, S.; Price, C., A link-based approach to the cluster ensemble problem, IEEE Trans. Pattern Anal. Mach. Intell., 33, 2396-2409 (2011) |
| [135] | Huang, D.; Lai, J. H.; Wang, C. D., Combining multiple clusterings via crowd agreement estimation and multi-granularity link analysis, Neurocomputing, 170, 240-250 (2015) |
| [136] | J.M. Duarte, A. Fred, F. Jorge, F. Duarte, Adaptive evidence accumulation clustering using the confidence of the objects’ assignments, in: Proceedings of PAKDD, 2013, pp. 70-87.; J.M. Duarte, A. Fred, F. Jorge, F. Duarte, Adaptive evidence accumulation clustering using the confidence of the objects’ assignments, in: Proceedings of PAKDD, 2013, pp. 70-87. |
| [137] | A. Lourenco, S.R. Bulo, A. Fred, M. Pelillo, Consensus clustering with robust evidence accumulation, in: Proceedings of International Conference on EMMCVPR, 2013, pp. 307-320.; A. Lourenco, S.R. Bulo, A. Fred, M. Pelillo, Consensus clustering with robust evidence accumulation, in: Proceedings of International Conference on EMMCVPR, 2013, pp. 307-320. |
| [138] | Y. Ren, C. Domeniconi, G. Zhang, G. Yu, Weighted-object ensemble clustering, in: Proceedings of IEEE International Conference on Data Mining, 2013, pp. 627-636.; Y. Ren, C. Domeniconi, G. Zhang, G. Yu, Weighted-object ensemble clustering, in: Proceedings of IEEE International Conference on Data Mining, 2013, pp. 627-636. |
| [139] | Berikov, V., Weighted ensemble of algorithms for complex data clustering, Pattern Recognit. Lett., 38, 99-106 (2014) |
| [140] | Zhong, C.; Yue, X.; Zhang, Z.; Lei, J., A clustering ensemble: Two-level-refined co-association matrix with path-based transformation, Pattern Recognit., 48, 2699-2709 (2015) · Zbl 1395.62179 |
| [141] | Louren, A.; Bulo, S. R.; Rebagliati, N.; Fred, A.; Figueiredo, M.; Pelillo, M., Probabilistic consensus clustering using evidence accumulation, Mach. Learn., 98, 331-357 (2015) · Zbl 1322.62174 |
| [142] | H. Liu, T. Liu, J. Wu, D. Tao, Y. Fu, Spectral ensemble clustering, in: Proceedings of ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, 2015, pp. 715-724.; H. Liu, T. Liu, J. Wu, D. Tao, Y. Fu, Spectral ensemble clustering, in: Proceedings of ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, 2015, pp. 715-724. |
| [143] | H. Wang, Y. Yang, H. Wang, D. Chen, Soft-voting clustering ensemble, in: Proceedings of International Workshop on Multiple Classifier Systems, 2013, pp. 307-318.; H. Wang, Y. Yang, H. Wang, D. Chen, Soft-voting clustering ensemble, in: Proceedings of International Workshop on Multiple Classifier Systems, 2013, pp. 307-318. |
| [144] | Sevillano, X.; Alas, F.; Socoro, J. C., Positional and confidence voting-based consensus functions for fuzzy cluster ensembles, Fuzzy Sets and Systems, 193, 1-32 (2012) |
| [145] | Zhang, L.; Ling, X. H.; Yang, J. W.; Wang, X. Q.; Li, F. Z., Cascaded cluster ensembles, Int. J. Mach. Learn. Cybern., 3, 335-343 (2012) |
| [146] | P. Zhou, L. Du, H. Wang, L. Shi, Y.D. Shen, Learning a robust consensus matrix for clustering ensemble via kullback-leibler divergence minimization, in: Proceedings of International Joint Conference on Artificial Intelligence, 2015, pp. 4112-4118.; P. Zhou, L. Du, H. Wang, L. Shi, Y.D. Shen, Learning a robust consensus matrix for clustering ensemble via kullback-leibler divergence minimization, in: Proceedings of International Joint Conference on Artificial Intelligence, 2015, pp. 4112-4118. |
| [147] | H. Li, H. Lin, J. Wu, G. Cheng, Bv-rsa: A rapid simulated annealing model for ensemble clustering, in: Proceedings of International Conference on Service Systems and Service Management, 2015, pp. 1-6.; H. Li, H. Lin, J. Wu, G. Cheng, Bv-rsa: A rapid simulated annealing model for ensemble clustering, in: Proceedings of International Conference on Service Systems and Service Management, 2015, pp. 1-6. |
| [148] | Christou, I. T., Coordination of cluster ensembles via exact methods, IEEE Trans. Pattern Anal. Mach. Intell., 33, 279-293 (2011) |
| [149] | S. Chatterjee, A. Mukhopadhyay, Clustering ensemble: A multiobjective genetic algorithm based approach, in: Proceedings of International Conference on Computational Intelligence: Modeling Techniques and Applications, 2013, pp. 443-449.; S. Chatterjee, A. Mukhopadhyay, Clustering ensemble: A multiobjective genetic algorithm based approach, in: Proceedings of International Conference on Computational Intelligence: Modeling Techniques and Applications, 2013, pp. 443-449. |
| [150] | Gullo, F.; Domeniconi, C.; Tagarelli, A., Metacluster-based projective clustering ensembles, Mach. Learn., 98, 181-216 (2015) · Zbl 1322.62169 |
| [151] | Franek, L.; Jiang, X., Ensemble clustering by means of clustering embedding in vector spaces, Pattern Recognit., 47, 833-842 (2014) · Zbl 1326.62137 |
| [152] | Bhatnagar, V.; Ahuja, S.; Kaur, S., Discriminant analysis-based cluster ensemble, Int. J. Data Mining Modell. Manage., 7, 83-107 (2015) |
| [153] | S. Xu, X. Li, R. Chen, S. Wu, J. Ni, Subspace similarity-based algorithm for combine multiple clustering, in: Proceedings of International Conference on Internet Computing for Engineering and Science, 2013, pp. 69-76.; S. Xu, X. Li, R. Chen, S. Wu, J. Ni, Subspace similarity-based algorithm for combine multiple clustering, in: Proceedings of International Conference on Internet Computing for Engineering and Science, 2013, pp. 69-76. |
| [154] | Vega-Pons, S.; Avesani, P., On pruning the search space for clustering ensemble problems, Neurocomputing, 150, 481-489 (2015) |
| [155] | Lock, E. F.; Dunson, D. B., Bayesian consensus clustering, Bioinformatics, 29, 2610-2616 (2013) |
| [156] | Yu, Z.; Li, L.; Wong, H. S.; You, J.; Han, G.; Gao, Y.; Yu, G., Probabilistic cluster structure ensemble, Inform. Sci., 267, 16-34 (2014) |
| [157] | Masson, M. H.; Denoeux, T., Ensemble clustering in the belief functions framework, Internat. J. Approx. Reason., 52, 92-109 (2011) · Zbl 1213.68501 |
| [158] | Y. Wu, X. Liu, L. Guo, A new ensemble clustering method based on dempster-shafer evidence theory and gaussian mixture modeling, 2014, pp. 1-8.; Y. Wu, X. Liu, L. Guo, A new ensemble clustering method based on dempster-shafer evidence theory and gaussian mixture modeling, 2014, pp. 1-8. |
| [159] | Zheng, L.; Li, T.; Ding, C., A framework for hierarchical ensemble clustering, ACM Trans. Knowl. Discov. Data, 9, 9 (2014) |
| [160] | H. Aidos, A. Fred, Consensus of clusterings based on high-order dissimilarities, in: Partitional Clustering Algorithms, 2015, pp. 313-351.; H. Aidos, A. Fred, Consensus of clusterings based on high-order dissimilarities, in: Partitional Clustering Algorithms, 2015, pp. 313-351. |
| [161] | Yan, D.; Chen, A.; Jordan, M. I., Cluster forests, Comput. Statist. Data Anal., 66, 178-192 (2013) · Zbl 1471.62225 |
| [162] | Xiao, W.; Yang, Y.; Wang, H.; Li, T.; Xing, H., Semi-supervised hierarchical clustering ensemble and its application, Neurocomputing, 173, 362-1376 (2016) |
| [163] | Mimaroglu, S.; Erdil, E., Combining multiple clusterings using similarity graph, Pattern Recognit., 44, 694-703 (2011) · Zbl 1209.68474 |
| [164] | D.D. Abdala, P. Wattuya, X. Jiang, Ensemble clustering via random walker consensus strategy, in: Proceedings of International Conference on Pattern Recognition, 2010, pp. 1433-1436.; D.D. Abdala, P. Wattuya, X. Jiang, Ensemble clustering via random walker consensus strategy, in: Proceedings of International Conference on Pattern Recognition, 2010, pp. 1433-1436. |
| [165] | A.H. Sadeghian, H. Nezamabadi-pour, Gravitational ensemble clustering, 2014, pp. 1-6.; A.H. Sadeghian, H. Nezamabadi-pour, Gravitational ensemble clustering, 2014, pp. 1-6. |
| [166] | L. Du, Y.D. Shen, Z. Shen, J. Wang, Z. Xu, A self-supervised framework for clustering ensemble, in: Proceedings of International Conference on Web-Age Information Management, 2013, pp. 253-264.; L. Du, Y.D. Shen, Z. Shen, J. Wang, Z. Xu, A self-supervised framework for clustering ensemble, in: Proceedings of International Conference on Web-Age Information Management, 2013, pp. 253-264. |
| [167] | R. Fa, B. Abu-Jamous, D.J. Roberts, A.K. Nandi, Coce-smart: Consensus clustering based on enhanced splitting-merging awareness tactics. in: Proceedings of IEEE International Conference on Acoustics, Speech and Signal Processing, 2015, pp. 2011-2015.; R. Fa, B. Abu-Jamous, D.J. Roberts, A.K. Nandi, Coce-smart: Consensus clustering based on enhanced splitting-merging awareness tactics. in: Proceedings of IEEE International Conference on Acoustics, Speech and Signal Processing, 2015, pp. 2011-2015. |
| [168] | Teng, G.; He, C.; Xiao, J.; He, Y.; Zhu, B.; Jiang, X., Cluster ensemble framework based on the group method of data handling, Appl. Soft Comput., 43, 35-46 (2016) |
| [169] | Ammour, N.; Alajlan, N., A dynamic weights owa fusion for ensemble clustering, Signal Image Video Process., 9, 727-734 (2015) |
| [170] | Rashedi, E.; Mirzaei, A.; Rahmati, M., Optimized aggregation function in hierarchical clustering combination, Intell. Data Anal., 20, 281-291 (2016) |
| [171] | Vahidipour, S. M.; Mirzaei, A.; Rahmati, M., Comparing weighted combination of hierarchical clustering based on cophenetic measure, Intell. Data Anal., 18, 547-559 (2014) |
| [172] | Rashedi, E.; Mirzaei, A.; Rahmati, M., An information theoretic approach to hierarchical clustering combination, Neurocomputing, 148, 487-497 (2015) |
| [173] | Fiori, A.; Mignone, A.; Rospo, G., Decoclu: Density consensus clustering approach for public transport data, Inform. Sci., 328, 378-388 (2016) |
| [174] | Haghtalab, S.; Xanthopoulos, P.; Madani, K., A robust unsupervised consensus control chart pattern recognition framework, Expert Syst. Appl., 42, 6767-6776 (2015) |
| [175] | Ramasso, E.; Placet, V.; Boubakar, M. L., Unsupervised consensus clustering of acoustic emission time-series for robust damage sequence estimation in composites, IEEE Trans. Instrum. Meas., 64, 3297-3307 (2015) |
| [176] | Yanga, Y.; Jiang, J., Hmm-based hybrid meta-clustering ensemble for temporal data, Knowl.-Based Syst., 36, 299-310 (2014) |
| [177] | L. Franek, D.D. Abdala, S. Vega-Pons, X. Jiang, Image segmentation fusion using general ensemble clustering methods, in: Asian Conference on Computer Vision, 2010, pp. 373-384.; L. Franek, D.D. Abdala, S. Vega-Pons, X. Jiang, Image segmentation fusion using general ensemble clustering methods, in: Asian Conference on Computer Vision, 2010, pp. 373-384. |
| [178] | H. Kim, J.J. Thiagarajan, P. Bremer, Image segmentation using consensus from hierarchical segmentation ensembles, in: Proceedings of IEEE International Conference on Image Processing 2014, pp. 3272 -3276.; H. Kim, J.J. Thiagarajan, P. Bremer, Image segmentation using consensus from hierarchical segmentation ensembles, in: Proceedings of IEEE International Conference on Image Processing 2014, pp. 3272 -3276. |
| [179] | Wang, X.; Du, J.; Wu, S.; Li, X.; Li, F., Cluster ensemble-based image segmentation, Int. J. Adv. Robot. Syst., 10, 297 (2013) |
| [180] | G. Akbarizadeh, M. Rahmani, A new ensemble clustering method for polsar image segmentation, in: Proceedings of International Conference on Information and Knowledge Technology, 2015, pp. 1-4.; G. Akbarizadeh, M. Rahmani, A new ensemble clustering method for polsar image segmentation, in: Proceedings of International Conference on Information and Knowledge Technology, 2015, pp. 1-4. |
| [181] | Saha, I.; Maulik, U.; Bandyopadhyay, S.; Plewczynski, D., Svmefc: Svm ensemble fuzzy clustering for satellite image segmentation, IEEE Geosci. Remote Sensing Lett., 9, 52-55 (2011) |
| [182] | S. Lewin, X. Jiang, A. Clausing, A clustering-based ensemble technique for shape decomposition. in: Proceedings of Joint IAPR International Workshop on Structural, Syntactic, and Statistical Pattern Recognition, 2012, pp. 153-161.; S. Lewin, X. Jiang, A. Clausing, A clustering-based ensemble technique for shape decomposition. in: Proceedings of Joint IAPR International Workshop on Structural, Syntactic, and Statistical Pattern Recognition, 2012, pp. 153-161. |
| [183] | Lopez-Sastre, R. J.; Renes-Olalla, J.; Gil-Jimenez, P.; Maldonado-Bascon, S.; Lafuente-Arroyo, S., Heterogeneous visual codebook integration via consensus clustering for visual categorization, IEEE Trans. Circuits Syst. Video Technol., 23, 1358-1368 (2013) |
| [184] | A. Lourenco, C. Carreiras, S.R. Bulo, A. Fred, Ecg analysis using consensus clustering, in: Proceedings of European Conference on Signal Processing, 2014, pp. 511-515.; A. Lourenco, C. Carreiras, S.R. Bulo, A. Fred, Ecg analysis using consensus clustering, in: Proceedings of European Conference on Signal Processing, 2014, pp. 511-515. |
| [185] | Bassiou, N.; Moschou, V.; Kotropoulos, C., Speaker diarization exploiting the eigengap criterion and cluster ensembles, IEEE Trans. Audio Speech Language Process., 18, 2134-2144 (2010) |
| [186] | Saeed, F.; Salim, N.; Abdo, A., Voting-based consensus clustering for combining multiple clusterings of chemical structures, J. Cheminform., 4, 37 (2012) |
| [187] | Saeed, F.; Salim, N.; Abdo, A., Information theory and voting based consensus clustering for combining multiple clusterings of chemical structures, Mole. Inform., 32, 591-598 (2013) |
| [188] | Saeed, F.; Ahmed, A.; Shamsir, M.; Salim, N., Weighted voting-based consensus clustering for chemical structure databases, J. Comput. Aided Mol. Des., 28, 675-684 (2014) |
| [189] | N.A. Chowdhury, D. Dou, Improving the accuracy of ontology alignment through ensemble fuzzy clustering. in: Proceedings of International Symposium on Distributed Objects, Middleware and Applications, 2011, p. 826-833.; N.A. Chowdhury, D. Dou, Improving the accuracy of ontology alignment through ensemble fuzzy clustering. in: Proceedings of International Symposium on Distributed Objects, Middleware and Applications, 2011, p. 826-833. |
| [190] | A.H. Sadeghian, H. Nezamabadi-pour, Document clustering using gravitational ensemble clustering, in: Proceedings of International Symposium on Artificial Intelligence and Signal Processing, 2015, pp. 240-245.; A.H. Sadeghian, H. Nezamabadi-pour, Document clustering using gravitational ensemble clustering, in: Proceedings of International Symposium on Artificial Intelligence and Signal Processing, 2015, pp. 240-245. |
| [191] | G. Costa, R. Ortale, Developments in partitioning xml documents by content and structure based on combining multiple clusterings, in: Proceedings of IEEE International Conference on Tools with Artificial Intelligence, 2013, pp. 477-482.; G. Costa, R. Ortale, Developments in partitioning xml documents by content and structure based on combining multiple clusterings, in: Proceedings of IEEE International Conference on Tools with Artificial Intelligence, 2013, pp. 477-482. |
| [192] | Carpineto, C.; Romano, G., Consensus clustering based on a new probabilistic rand index with application to subtopic retrieval, IEEE Trans. Pattern Anal. Mach. Intell., 34, 2315-2326 (2012) |
| [193] | H. Aidos, C. Carreiras, H. Silva, A. Fred, Evidence accumulation approach applied to eeg analysis, in: Proceedings of International Conference on Pattern Recognition Applications and Methods, 2013, pp. 479-484.; H. Aidos, C. Carreiras, H. Silva, A. Fred, Evidence accumulation approach applied to eeg analysis, in: Proceedings of International Conference on Pattern Recognition Applications and Methods, 2013, pp. 479-484. |
| [194] | Mahrooghy, M.; Younan, N. H.; Anantharaj, V. G.; Aanstoos, J.; Yarahmadian, S., On the use of a cluster ensemble cloud classification technique in satellite precipitation estimation, IEEE J. Sel. Top. Appl. Earth Obs. Remote Sens., 5, 1356-1363 (2012) |
| [195] | Y. Moazzen, B. Yalcin, K. Tasdemir, Sampling based approximate spectral clustering ensemble for unsupervised land cover identification. in: Proceedings of IEEE International Symposium on Geoscience and Remote Sensing, 2015, pp. 2405-2408.; Y. Moazzen, B. Yalcin, K. Tasdemir, Sampling based approximate spectral clustering ensemble for unsupervised land cover identification. in: Proceedings of IEEE International Symposium on Geoscience and Remote Sensing, 2015, pp. 2405-2408. |
| [196] | Tasdemir, K.; Moazzen, Y.; Yildirim, I., An approximate spectral clustering ensemble for high spatial resolution remote-sensing images, IEEE J. Sel. Top. Appl. Earth Obs. Remote Sens., 8, 1996-2004 (2015) |
| [197] | W. Gu, Z. Zhang, B. Wang, X. Wang, Use of ontology and cluster ensembles for geospatial clustering analysis, in: Proceedings of Canadian Conference on Artificial Intelligence, 2014, pp. 119-130.; W. Gu, Z. Zhang, B. Wang, X. Wang, Use of ontology and cluster ensembles for geospatial clustering analysis, in: Proceedings of Canadian Conference on Artificial Intelligence, 2014, pp. 119-130. |
| [198] | Yu, Z.; Li, L.; You, J.; Wong, H. S.; Han, G., Sc3: Triple spectral clustering-based consensus clustering framework for class discovery from cancer gene expression profiles, IEEE/ACM Trans. Comput. Biol. Bioinform., 9, 1751-1765 (2012) |
| [199] | Yang, L. O.; Dai, D. Q.; Zhang, X. F., Protein complex detection via weighted ensemble clustering based on bayesian nonnegative matrix factorization, PLOS ONE, 8, e62158 (2013) |
| [200] | Wang, C.; Machiraju, R.; Huang, K., Breast cancer patient stratification using a molecular regularized consensus clustering method, Methods, 67, 304-312 (2014) |
| [201] | H. Aidos, A. Lourenco, D. Batista, S.R. Bulo, A. Fred, Semi-supervised consensus clustering for ecg pathology classification, in: European Conference on Machine Learning and Knowledge Discovery in Databases, 2015, pp. 150-164.; H. Aidos, A. Lourenco, D. Batista, S.R. Bulo, A. Fred, Semi-supervised consensus clustering for ecg pathology classification, in: European Conference on Machine Learning and Knowledge Discovery in Databases, 2015, pp. 150-164. |
| [202] | Yang, P.; Su, X.; Yang, L. O.; Chua, H. N.; Li, X. L.; Ning, K., Microbial community pattern detection in human body habitats via ensemble clustering framework, BMC Syst. Biol., 8, 57 (2014) |
| [203] | Ahuja, S., Regionalization of river basins using cluster ensemble, J. Water Resour. Protect., 4, 560-566 (2012) |
| [204] | Bognera, C.; Widemann, B. T.; Lange, H., Characterising flow patterns in soils by feature extraction and multiple consensus clustering, Ecol. Inform., 15, 44-52 (2013) |
| [205] | Y. Ye, T. Li, Y. Chen, Q. Jiang, Automatic malware categorization using cluster ensemble. in: Proceedings of ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, 2010, pp. 95-104.; Y. Ye, T. Li, Y. Chen, Q. Jiang, Automatic malware categorization using cluster ensemble. in: Proceedings of ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, 2010, pp. 95-104. |
| [206] | X. Hu, K.G. Shin, Duet: integration of dynamic and static analyses for malware clustering with cluster ensembles, in: Proceedings of Annual Conference on Computer Security Applications, 2013, pp. 79-88.; X. Hu, K.G. Shin, Duet: integration of dynamic and static analyses for malware clustering with cluster ensembles, in: Proceedings of Annual Conference on Computer Security Applications, 2013, pp. 79-88. |
| [207] | S. Hou, L. Chen, E. Tas, I. Demihovskiy, Y. Ye, Cluster-oriented ensemble classifiers for intelligent malware detection, in: IEEE International Conference on Semantic Computing, 2015, pp. 189-196.; S. Hou, L. Chen, E. Tas, I. Demihovskiy, Y. Ye, Cluster-oriented ensemble classifiers for intelligent malware detection, in: IEEE International Conference on Semantic Computing, 2015, pp. 189-196. |
| [208] | Zhuang, W.; Ye, Y.; Chen, Y.; Li, T., Ensemble clustering for internet security applications, IEEE Trans. Syst. Man Cybern. C, 42, 1784-1796 (2012) |
| [209] | Kim, E. Y.; Hwang, D. U.; Ko, T. W., Multiscale ensemble clustering for finding modules in complex networks, Phys. Rev. E, 85, 026119 (2012) |
| [210] | Lancichinetti, A.; Fortunato, S., Consensus clustering in complex networks, Sci. Rep., 2, 336 (2012) |
| [211] | Zhao, W.; Liu, H.; Dai, W.; Ma, J., An entropy-based clustering ensemble method to support resource allocation in business process management, Knowl. Inform. Syst., 48, 305-330 (2016) |
| [212] | Canali, C.; Lancellotti, R., Exploiting ensemble techniques for automatic virtual machine clustering in cloud systems, Automat. Softw. Eng., 21, 319-344 (2014) |
| [213] | Jurek, A.; Nugent, C.; Bi, Y.; Wu, S., Clustering-based ensemble learning for activity recognition in smart homes, Sensors, 14, 12285-12304 (2014) |
| [214] | A. Acharya, E.R. Hruschka, J. Ghosh, S. Acharyya, Transfer learning with cluster ensembles, in: Proceedings of Workshop on Unsupervised and Transfer Learning, 2012, pp. 123-133.; A. Acharya, E.R. Hruschka, J. Ghosh, S. Acharyya, Transfer learning with cluster ensembles, in: Proceedings of Workshop on Unsupervised and Transfer Learning, 2012, pp. 123-133. |
| [215] | A. Albalate, A. Suchindranath, M.M. Soenmez, D. Suendermann, On ambiguity detection and postprocessing schemes using cluster ensembles, in: Proceedings of International Conference on Agents and Artificial Intelligence, 2010, pp. 623-630.; A. Albalate, A. Suchindranath, M.M. Soenmez, D. Suendermann, On ambiguity detection and postprocessing schemes using cluster ensembles, in: Proceedings of International Conference on Agents and Artificial Intelligence, 2010, pp. 623-630. |
| [216] | D. Rani, T. Rani, S. Bhavani, Consensus clustering for dimensionality reduction, in: Proceedings of International Conference on Contemporary Computing, 2014, pp. 148-153.; D. Rani, T. Rani, S. Bhavani, Consensus clustering for dimensionality reduction, in: Proceedings of International Conference on Contemporary Computing, 2014, pp. 148-153. |
| [217] | Wang, Y.; Pan, Y., Semi-supervised consensus clustering for gene expression data analysis, BMC BioData Mining, 7, 7 (2014) |
| [218] | Yu, Z.; Wong, H. S.; You, J.; Yang, Q.; Liao, H., Knowledge based cluster ensemble for cancer discovery from biomolecular data, IEEE Trans. NanoBiosci., 10, 76-85 (2011) |
| [219] | Yang, Y.; Tan, W.; Li, T.; Ruan, D., Consensus clustering based on constrained self-organizing map and improved cop-kmeans ensemble in intelligent decision support systems, Knowl.-Based Syst., 32, 101-115 (2012) |
| [220] | J. Zhang, Y. Yang, H. Wang, A. Mahmood, F. Huang, Semi-supervised clustering ensemble based on collaborative training, in: Proceedings of International Conference on Rough Sets and Knowledge Technology, 2012, pp. 450-455.; J. Zhang, Y. Yang, H. Wang, A. Mahmood, F. Huang, Semi-supervised clustering ensemble based on collaborative training, in: Proceedings of International Conference on Rough Sets and Knowledge Technology, 2012, pp. 450-455. |
| [221] | Yu, Z.; Luo, P.; You, J.; Wong, H. S.; Leung, H.; Wu, S.; Zhang, J.; Han, G., Incremental semi-supervised clustering ensemble for high dimensional data clustering, IEEE Trans. Knowl. Data Eng., 28, 701-714 (2016) |
| [222] | M.A. Duval-Poo, J. Sosa-Garcia, A. Guerra-Gandon, S. Vega-Pons, J. Ruiz-Shulcloper, A new classifier combination scheme using clustering ensemble. in: Proceedings of Iberoamerican Congress on Pattern Recognition, 2012, pp. 154-161.; M.A. Duval-Poo, J. Sosa-Garcia, A. Guerra-Gandon, S. Vega-Pons, J. Ruiz-Shulcloper, A new classifier combination scheme using clustering ensemble. in: Proceedings of Iberoamerican Congress on Pattern Recognition, 2012, pp. 154-161. |
| [223] | H.H. Nguyen, N. Harbi, J. Darmont, An efficient fuzzy clustering-based approach for intrusion detection, in: Proceedings of IEEE International Conference on Data Mining, 2011, pp. 607-612.; H.H. Nguyen, N. Harbi, J. Darmont, An efficient fuzzy clustering-based approach for intrusion detection, in: Proceedings of IEEE International Conference on Data Mining, 2011, pp. 607-612. |
| [224] | Sang-Woon, K., A pre-clustering technique for optimizing subclass discriminant analysis, Pattern Recognit. Lett., 31, 462-468 (2010) |
| [225] | G. Nasierding, G. Tsoumakas, A.Z. Kouzani, Clustering based multi-label classification for image annotation and retrieval, in: Proceedings of IEEE International Conference on System, Man and Cybernetics, 2009, pp. 4514-4519.; G. Nasierding, G. Tsoumakas, A.Z. Kouzani, Clustering based multi-label classification for image annotation and retrieval, in: Proceedings of IEEE International Conference on System, Man and Cybernetics, 2009, pp. 4514-4519. |
| [226] | N. Iam-On, T. Boongoen, Revisiting link-based cluster ensembles for microarray data classification, in: Proceedings of IEEE Systems, Man and Cybernetics, 2013, pp. 4543-4548.; N. Iam-On, T. Boongoen, Revisiting link-based cluster ensembles for microarray data classification, in: Proceedings of IEEE Systems, Man and Cybernetics, 2013, pp. 4543-4548. |
| [227] | Iam-On, N.; Boongoen, T.; Garrett, S.; Price, C., New cluster ensemble approach to integrative biological data analysis, Int. J. Data Mining Bioinform., 8, 150-168 (2013) |
| [228] | Blomstedt, P.; Tang, J.; Xiong, J.; Granlund, C.; Corander, J., A bayesian predictive model for clustering data of mixed discrete and continuous type, IEEE Trans. Pattern Anal. Mach. Intell., 37, 489-498 (2015) |
| [229] | Lam, D.; Wei, M.; Wunsch, D., Clustering data of mixed categorical and numerical type with unsupervised feature learning, IEEE Access, 3, 1605-1613 (2015) |
| [230] | Abidin, F. N.Z.; Westhead, D. R., Flexible model-based clustering of mixed binary and continuous data: Application to genetic regulation and cancer, Nucleic Acids Res., gkw1270, 1-11 (2016) |
| [231] | Fahad, A.; Alshatri, N.; Tari, Z.; Alamri, A.; Khalil, I.; Zomaya, A. Y.; Foufou, S.; Bouras, A., A survey of clustering algorithms for big data: Taxonomy and empirical analysis, IEEE Trans. Emerg. Top. Comput., 2, 267-279 (2014) |
| [232] | Su, P.; Shang, C.; Shen, Q., A hierarchical fuzzy cluster ensemble approach and its application to big data clustering, J. Intell. Fuzzy Syst., 28, 2409-2421 (2015) |
| [233] | Pestunova, I. A.; Berikovb, V. B.; Kulikovaa, E. A.; Rylova, S. A., Ensemble of clustering algorithms for large datasets, Optoelectron. Instrum. Data Process., 47, 45-252 (2011) |
| [234] | Ye, M.; Liu, W.; Wei, J.; Hu, X., Fuzzy c-means and cluster ensemble with random projection for big data clustering, Math. Probl. Eng., 2016, 1-13 (2016) · Zbl 1400.62140 |
| [235] | Swedlow, J. R.; Zanetti, G.; Best, C., Channeling the data deluge, Nat. Methods, 8, 463-465 (2011) |
| [236] | Dean, J.; Ghemawat, S., Mapreduce: A flexible data processing tool, Commun. ACM, 53, 72-77 (2010) |
| [237] | Liu, Y.; Yang, J.; Huang, Y.; Xu, L.; Li, S.; Qi, M., Mapreduce based parallel neural networks in enabling large scale machine learning, Comput. Intell. Neurosci., 2015, 1-13 (2015) |
| [238] | Banharnsakun, A., A mapreduce-based artificial bee colony for large-scale data clustering, Pattern Recognit. Lett., 93, 78-84 (2017) |
| [239] | V. Priya, K. Umamaheswari, Ensemble based parallel k means using map reduce for aspect based summarization, in: Proceedings of the International Conference on Informatics and Analytics, 2016, pp. 1-9.; V. Priya, K. Umamaheswari, Ensemble based parallel k means using map reduce for aspect based summarization, in: Proceedings of the International Conference on Informatics and Analytics, 2016, pp. 1-9. |
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