Frequent mining of subgraph structures. (English) Zbl 1111.68100
Summary: In recent years, frequent graph pattern mining has raised great interest in the data mining community and has been applied to bioinformatics, web exploration, etc. The difficulty is that the search of possible subgraph patterns is combinatorially explosive. Many researchers proposed methods to resolve this problem, such as FSG, gSpan, and CloseGraph. But we discovered that the result set generated by these methods contains many repetitive structures. In this article, we propose a new method called Frequent Structure Mining Algorithm (FSMA) to find frequent structure in graph database, and prove corresponding theoretics to guarantee the accuracy of FSMA. The power of FSMA comes from the resolution of finding different structures and the storage of the graph set; it guarantees the completeness of mining results.
Keywords:
data mining; graph structured mining; frequent subgraph; frequent structure; frequent structure mining algorithmReferences:
| [1] | Cook J, J. Artif. Intell. Res. 1 pp 231– (1994) |
| [2] | DOI: 10.1007/BF00872095 · doi:10.1007/BF00872095 |
| [3] | DOI: 10.1023/A:1009863704807 · doi:10.1023/A:1009863704807 |
| [4] | Nijssen S, Proceedings of the 17th International Joint Conference on Artificial Intelligence 2 pp 891– (2001) |
| [5] | De Readt L, Proceedings of the 17th International Joint Conference on Artificial Intelligence 2 pp 853– (2001) |
| [6] | Inokuchi I, Proceedings of the 4th European Conference on Principles and Practice of Knowledge Discovery in Databases pp 13– (2000) |
| [7] | Inokuchi A, IBM Research (2002) |
| [8] | DOI: 10.1109/ICDM.2001.989534 · doi:10.1109/ICDM.2001.989534 |
| [9] | Yan X, Proceedings of the IEEE 2002 International Conference on Data Mining pp 721– (2002) |
| [10] | DOI: 10.1145/956750.956784 · doi:10.1145/956750.956784 |
| [11] | Messmer BT, Technical Report IAM 95-003 |
| [12] | DOI: 10.1023/A:1021726221443 · Zbl 1033.68079 · doi:10.1023/A:1021726221443 |
| [13] | DOI: 10.1145/1186810.1186811 · Zbl 1445.68012 · doi:10.1145/1186810.1186811 |
| [14] | DOI: 10.1016/0743-1066(94)90035-3 · Zbl 0816.68043 · doi:10.1016/0743-1066(94)90035-3 |
| [15] | Antunes C, Proceedings of the International Conference on Machine Learning and Data Mining pp 239– (2003) |
This reference list is based on information provided by the publisher or from digital mathematics libraries. Its items are heuristically matched to zbMATH identifiers and may contain data conversion errors. In some cases that data have been complemented/enhanced by data from zbMATH Open. This attempts to reflect the references listed in the original paper as accurately as possible without claiming completeness or a perfect matching.
