Three-Way Clustering Method Based on Stability Theory

Wang, Pingxin; Yang, Xibei

doi:10.1109/access.2021.3057405

Cited by 24 publications

(12 citation statements)

References 56 publications

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“…The proposed algorithm PROCK is a modification of ROCK [24]; hence, both performances should be compared. Taking the results published in [25,26] for the Congressional Votes dataset, Tab.…”

Section: Comparison With Rockmentioning

confidence: 99%

P-ROCK: A Sustainable Clustering Algorithm for Large Categorical Datasets

Altameem¹,

Poonia²,

Kumar³

et al. 2023

Intelligent Automation &Amp; Soft Computing

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Data clustering is crucial when it comes to data processing and analytics. The new clustering method overcomes the challenge of evaluating and extracting data from big data. Numerical or categorical data can be grouped. Existing clustering methods favor numerical data clustering and ignore categorical data clustering. Until recently, the only way to cluster categorical data was to convert it to a numeric representation and then cluster it using current numeric clustering methods. However, these algorithms could not use the concept of categorical data for clustering. Following that, suggestions for expanding traditional categorical data processing methods were made. In addition to expansions, several new clustering methods and extensions have been proposed in recent years. ROCK is an adaptable and straightforward algorithm for calculating the similarity between data sets to cluster them. This paper aims to modify the algorithm by creating a parameterized version that takes specific algorithm parameters as input and outputs satisfactory cluster structures. The parameterized ROCK algorithm is the name given to the modified algorithm (P-ROCK). The proposed modification makes the original algorithm more flexible by using user-defined parameters. A detailed hypothesis was developed later validated with experimental results on real-world datasets using our proposed P-ROCK algorithm. A comparison with the original ROCK algorithm is also provided. Experiment results show that the proposed algorithm is on par with the original ROCK algorithm with an accuracy of 97.9%. The proposed P-ROCK algorithm has improved the runtime and is more flexible and scalable.

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“…The proposed algorithm PROCK is a modification of ROCK [24]; hence, both performances should be compared. Taking the results published in [25,26] for the Congressional Votes dataset, Tab.…”

Section: Comparison With Rockmentioning

confidence: 99%

P-ROCK: A Sustainable Clustering Algorithm for Large Categorical Datasets

Altameem¹,

Poonia²,

Kumar³

et al. 2023

Intelligent Automation &Amp; Soft Computing

View full text Add to dashboard Cite

show abstract

“…The idea of three-way clustering is to use a pair of sets to represent each cluster. This pair of sets consists of core region (Co), fringe region (Fr), and trivial fringe (Tr) [43]. Then, the three-way cluster is expressed as,…”

Section: Three-way Clusteringmentioning

confidence: 99%

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

Fan

Wang

et al. 2022

Mathematics

Self Cite

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The complexity of the data type and distribution leads to the increase in uncertainty in the relationship between samples, which brings challenges to effectively mining the potential cluster structure of data. Ensemble clustering aims to obtain a unified cluster division by fusing multiple different base clustering results. This paper proposes a three-way ensemble clustering algorithm based on sample’s perturbation theory to solve the problem of inaccurate decision making caused by inaccurate information or insufficient data. The algorithm first combines the natural nearest neighbor algorithm to generate two sets of perturbed data sets, randomly extracts the feature subsets of the samples, and uses the traditional clustering algorithm to obtain different base clusters. The sample’s stability is obtained by using the co-association matrix and determinacy function, and then the samples can be divided into a stable region and unstable region according to a threshold for the sample’s stability. The stable region consists of high-stability samples and is divided into the core region of each cluster using the K-means algorithm. The unstable region consists of low-stability samples and is assigned to the fringe regions of each cluster. Therefore, a three-way clustering result is formed. The experimental results show that the proposed algorithm in this paper can obtain better clustering results compared with other clustering ensemble algorithms on the UCI Machine Learning Repository data set, and can effectively reveal the clustering structure.

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“…These two sets divide the universe into three parts, where there are three types of relationships between the objects and cluster, namely objects belonging to the cluster, objects not belonging to the cluster, and objects partially belonging to the cluster. Recently, three-way clustering has attracted much research, and many three-way clustering algorithms have been developed [48][49][50][51][52][53][54][55][56].…”

Section: Introductionmentioning

confidence: 99%

An Improved Three-Way Clustering Based on Ensemble Strategy

Fan

Wang

2022

Mathematics

Self Cite

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As a powerful data analysis technique, clustering plays an important role in data mining. Traditional hard clustering uses one set with a crisp boundary to represent a cluster, which cannot solve the problem of inaccurate decision-making caused by inaccurate information or insufficient data. In order to solve this problem, three-way clustering was presented to show the uncertainty information in the dataset by adding the concept of fringe region. In this paper, we present an improved three-way clustering algorithm based on an ensemble strategy. Different to the existing clustering ensemble methods by using various clustering algorithms to produce the base clustering results, the proposed algorithm randomly extracts a feature subset of samples and uses the traditional clustering algorithm to obtain the diverse base clustering results. Based on the base clustering results, labels matching is used to align all clustering results in a given order and voting method is used to obtain the core region and the fringe region of the three way clustering. The proposed algorithm can be applied on the top of any existing hard clustering algorithm to generate the base clustering results. As examples for demonstration, we apply the proposed algorithm on the top of K-means and spectral clustering, respectively. The experimental results show that the proposed algorithm is effective in revealing cluster structures.

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Three-Way Clustering Method Based on Stability Theory

Cited by 24 publications

References 56 publications

P-ROCK: A Sustainable Clustering Algorithm for Large Categorical Datasets

P-ROCK: A Sustainable Clustering Algorithm for Large Categorical Datasets

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

An Improved Three-Way Clustering Based on Ensemble Strategy

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