Three-Way Ensemble Clustering Based on Sample’s Perturbation Theory

Fan, Jiachen; Wang, Xiaoxiao; Wu, Tingfeng; Zhu, Jin; Wang, Pingxin

doi:10.3390/math10152598

Cited by 2 publications

(3 citation statements)

References 54 publications

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“…Shah [40] proposed a new three-way clustering by using image inspired cluster blur and sharp operators. Except the above research results, some other algorithms also enrich the theories and models of three-way clustering [41][42][43][44][45].…”

Section: Three-way Clusteringmentioning

confidence: 96%

An Improved Three-Way K-Means Algorithm by Optimizing Cluster Centers

2022

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Most of data set can be represented in an asymmetric matrix. How to mine the uncertain information from the matrix is the primary task of data processing. As a typical unsupervised learning method, three-way k-means clustering algorithm uses core region and fringe region to represent clusters, which can effectively deal with the problem of inaccurate decision-making caused by inaccurate information or insufficient data. However, same with k-means algorithm, three-way k-means also has the problems that the clustering results are dependent on the random selection of clustering centers and easy to fall into the problem of local optimization. In order to solve this problem, this paper presents an improved three-way k-means algorithm by integrating ant colony algorithm and three-way k-means. Through using the random probability selection strategy and the positive and negative feedback mechanism of pheromone in ant colony algorithm, the sensitivity of the three k-means clustering algorithms to the initial clustering center is optimized through continuous updating iterations, so as to avoid the clustering results easily falling into local optimization. Dynamically adjust the weights of the core domain and the boundary domain to avoid the influence of artificially set parameters on the clustering results. The experiments on UCI data sets show that the proposed algorithm can improve the performances of three-way k-means clustering results and is effective in revealing cluster structures.

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Section: Three-way Clusteringmentioning

confidence: 96%

An Improved Three-Way K-Means Algorithm by Optimizing Cluster Centers

2022

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“…The co-association frequency matrix of Figure 3. Co-association frequency [57,58] is to measure the probability that two data samples are assigned to the same cluster in multiple clustering results. Specifically, if two samples are consistently assigned to the same cluster across multiple clustering results, their co-Data set X According to the above definition, we can obtain the co-association matrix of Figure 3 as Table 1.…”

Section: Clustering Ensemble and Co-association Frequencymentioning

confidence: 99%

“…Specifically, if two samples are consistently assigned to the same cluster across multiple clustering results, their co-Data set X According to the above definition, we can obtain the co-association matrix of Figure 3 as Table 1. Co-association frequency [57,58] is to measure the probability that two data samples are assigned to the same cluster in multiple clustering results. Specifically, if two samples are consistently assigned to the same cluster across multiple clustering results, their coassociation frequency is 1.…”

Section: Clustering Ensemble and Co-association Frequencymentioning

confidence: 99%

Similarity-Based Three-Way Clustering by Using Dimensionality Reduction

Li,

Meng,

Wang

2024

Mathematics

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Three-way clustering uses core region and fringe region to describe a cluster, which divide the dataset into three parts. The division helps identify the central core and outer sparse regions of a cluster. One of the main challenges in three-way clustering is the meaningful construction of the two sets. Aimed at handling high-dimensional data and improving the stability of clustering, this paper proposes a novel three-way clustering method. The proposed method uses dimensionality reduction techniques to reduce data dimensions and eliminate noise. Based on the reduced dataset, random sampling and feature extraction are performed multiple times to introduce randomness and diversity, enhancing the algorithm’s robustness. Ensemble strategies are applied on these subsets, and the k-means algorithm is utilized to obtain multiple clustering results. Based on these results, we obtain co-association frequency between different samples and fused clustering result using the single-linkage method of hierarchical clustering. In order to describe the core region and fringe region of each cluster, the similar class of each sample is defined by co-association frequency. The lower and upper approximations of each cluster are obtained based on similar class. The samples in the lower approximation of each cluster belong to the core region of the cluster. The differences between lower and upper approximations of each cluster are defined as fringe region. Therefore, a three-way explanation of each cluster is naturally formed. By employing various UC Irvine Machine Learning Repository (UCI) datasets and comparing different clustering metrics such as Normalized Mutual Information (NMI), Adjusted Rand Index (ARI), and Accuracy (ACC), the experimental results show that the proposed strategy is effective in improving the structure of clustering results.

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Three-Way Ensemble Clustering Based on Sample’s Perturbation Theory

Cited by 2 publications

References 54 publications

An Improved Three-Way K-Means Algorithm by Optimizing Cluster Centers

An Improved Three-Way K-Means Algorithm by Optimizing Cluster Centers

Similarity-Based Three-Way Clustering by Using Dimensionality Reduction

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