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Reduction Through Homogeneous Clustering: Variations for Categorical Data and Fast Data Reduction
Reduction through Homogeneous Clustering (RHC) and its editing variant (ERHC) represent effective methods for reducing data in the context of...
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Consistency-oriented clustering ensemble via data reconstruction
The study highlights that using different distance measures on the same dataset leads to varying clustering results, making the choice of distance...
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An Improved Water Flow Optimizer for Data Clustering
Recently, various meta-heuristic algorithms have been considered to allocate the data into different clusters based on similar information. These...
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Clustering-based visualizations for diagnosing diseases on metagenomic data
Metagenomic data has recently become crucial for precision or personalized medicine. However, these data are often complex, challenging to observe...
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Split incremental clustering algorithm of mixed data stream
Clustering has been recognized as one of the most prominent functions in data mining. It aims to partition a given set of elements into homogeneous...
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Clustering from Data Streams
Clustering is one of the most popular data mining techniques. In this article, we review the relevant methods and algorithms for designing cluster...
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Semi-supervised sparse representation collaborative clustering of incomplete data
Sparse subspace clustering (SSC) focuses on revealing the structure and distribution of high dimensional data from an algebraic perspective. It is a...
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Robust and compact maximum margin clustering for high-dimensional data
In the field of machine learning, clustering has become an increasingly popular research topic due to its critical importance. Many clustering...
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Randomized self-updating process for clustering large-scale data
This paper introduces the randomized self-updating process (rSUP) algorithm for clustering large-scale data. rSUP is an extension of the...
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An effective clustering scheme for high-dimensional data
While the classical K -means algorithm has been widely used in many fields, it still has some defects. Therefore, this paper proposes a scheme to...
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DDSC-SMOTE: an imbalanced data oversampling algorithm based on data distribution and spectral clustering
Imbalanced data poses a significant challenge in machine learning, as conventional classification algorithms often prioritize majority class samples,...
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Density-Based Clustering for Incomplete Data
In real world, missing values exist in a lot of data sets and cause data incompleteness. However, traditional missing value imputation methods are...
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A Survey and Experimental Review on Data Distribution Strategies for Parallel Spatial Clustering Algorithms
The advent of Big Data has led to the rapid growth in the usage of parallel clustering algorithms that work over distributed computing frameworks...
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Efficient Clustering on Encrypted Data
Clustering is a significant unsupervised machine learning task widely used for data mining and analysis. Fully homomorphic encryption allows data...
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Penalized model-based clustering of complex functional data
High dimensional data, large-scale data, imaging and manifold data are all fostering new frontiers of statistics. These type of data are commonly...
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Model-based clustering with missing not at random data
Model-based unsupervised learning, as any learning task, stalls as soon as missing data occurs. This is even more true when the missing data are...
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Explainable AI for Mixed Data Clustering
Clustering, an unsupervised machine learning approach, aims to find groups of similar instances. Mixed data clustering is of particular interest...
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Data clustering: application and trends
Clustering has primarily been used as an analytical technique to group unlabeled data for extracting meaningful information. The fact that no...
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CPOCEDS-concept preserving online clustering for evolving data streams
Clustering streaming data is challenging due to many temporal dynamics, such as concept drift, concept evolution, and feature evolution. Concept...
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An efficient meta-heuristic algorithm based on water flow optimizer for data clustering
Clustering is a popular data analysis technique that can explore the structure of data through cluster analysis. Similar data are put into the same...
