Fuzzy rough clustering for categorical data

Xu, Shengli; Liu, Shenglan; Zhou, Jian

doi:10.1007/s13042-019-01012-6

Cited by 9 publications

(3 citation statements)

References 30 publications

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“…The publication trend shows an increase, with 2019 emerging as the most productive year. In that year, 11 articles related to clustering were published, covering hierarchical-based [47,48], rough-set-based [49,50], weight-based [51], graph-based [52], a variant of fuzzy clustering [53][54][55], integer linear programming [56], and clustering validity [57]. Moreover, for a deeper comprehension of the citations and publications spanning the period from 2014 to 2023, Table 3 presents the top ten cited articles.…”

Section: • Publication Yearsmentioning

confidence: 99%

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Categorical Data Clustering: A Bibliometric Analysis and Taxonomy

Cendana,

Kuo

2024

MAKE

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Numerous real-world applications apply categorical data clustering to find hidden patterns in the data. The K-modes-based algorithm is a popular algorithm for solving common issues in categorical data, from outlier and noise sensitivity to local optima, utilizing metaheuristic methods. Many studies have focused on increasing clustering performance, with new methods now outperforming the traditional K-modes algorithm. It is important to investigate this evolution to help scholars understand how the existing algorithms overcome the common issues of categorical data. Using a research-area-based bibliometric analysis, this study retrieved articles from the Web of Science (WoS) Core Collection published between 2014 and 2023. This study presents a deep analysis of 64 articles to develop a new taxonomy of categorical data clustering algorithms. This study also discusses the potential challenges and opportunities in possible alternative solutions to categorical data clustering.

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Section: • Publication Yearsmentioning

confidence: 99%

“…Moreover, Xu et al introduced a fuzzy rough clustering (FRC) algorithm [49] based on RST, combining information granularity and dimension reduction. FRC employs a weighted distance metric to measure dissimilarity in categorical datasets by converting them into numerical datasets.…”

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confidence: 99%

Categorical Data Clustering: A Bibliometric Analysis and Taxonomy

Cendana,

Kuo

2024

MAKE

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“…8 In order to lift the speed of the clustering algorithm, Maji et al proposed the concept of inhibitors. 9 Clustering algorithms have also begun to be applied in different fields, 10 such as the field of image segmentation, 11 data analysis, [12][13][14] and medicine. [15][16][17] Since the complete information of a given set of patterns is often not obtained in practical applications, the uncertainty arises in various pattern recognitions.…”

Section: Related Workmentioning

confidence: 99%

A novel fuzzy clustering algorithm based on rough set and inhibitive factor

Tang

Wang

et al. 2020

Concurrency and Computation

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As an important data analysis method, rough set theory can be used to analyze data with uncertainties. Rough set is able to acquire knowledge by the indistinguishable relationship among data objects without any prior knowledge. Rough set theory provides a new theoretical means for solving soft computing problems and has a wide application space in data mining. Meanwhile, the fuzzy C-means clustering algorithm is sensitive to noisy points and has low convergence speed. In order to deal with the above problems, a novel fuzzy clustering algorithm based on rough set and inhibitive factor is proposed. According to the related concepts of rough set theory, the membership model of fuzzy C-means algorithm is redefined. Besides, an inhibitive factor is set to improve the convergence speed of the algorithm under the premise of guaranteeing the clustering effect. K E Y W O R D S fuzzy clustering, inhibitive factor, membership degree, rough set 1 INTRODUCTION There are many cluster analysis approaches in the data clustering field. From the view of the membership degrees of data sample, cluster analysis can be divided into two categories. One is the hard clustering analysis, the other is the soft clustering analysis, also known as fuzzy clustering analysis. 1 Hard clustering is a kind of strict division, which aims to divide every data sample into a specific cluster. Its division mechanism is very clear with only two membership degrees: 0 and 1, which means a sample can only belong to a certain class or not at all. This clustering method is too strict to deal with some data at the boundary, which could easily cause errors. Soft clustering is more tolerant for the division of data and divides them into different clusters according to their membership degree. The traditional fuzzy clustering method is sensitive to noise data, and the accuracy of clustering is often not good enough when dealing with complex data sets. Although some data objects are divided into a certain cluster, it is not obvious that they belong to that one. Rough set theory can deal with the fuzzy and uncertain problems. The idea of inhibitive factor comes from the suppressed fuzzy C-means clustering algorithm (S-FCM). 2 The essence of S-FCM is prizing the biggest membership and suppressing the others. By choosing the reasonable a parameter, S-FCM converges faster than FCM under the precondition of not decreasing clustering performance. In this article, the related work about fuzzy clustering algorithm based on rough set is overviewed in Section 2. In Section 3 the related concepts of rough set theory and fuzzy method are introduced. Section 4 designs a new calculation method of fuzzy membership degree. At the same time, an inhibitive factor is used to improve the convergence speed of the algorithm, and then a fuzzy clustering algorithm based on rough set and the inhibitive factor is proposed. The experimental results and analysis are given in Section 6. Section 6 gives the conclusion of this article.

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Fuzzy K-means clustering with fast density peak clustering on multivariate kernel estimator with evolutionary multimodal optimization clusters on a large dataset

Narayana¹,

Kolli²

2020

Multimed Tools Appl

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Fuzzy rough clustering for categorical data

Cited by 9 publications

References 30 publications

Categorical Data Clustering: A Bibliometric Analysis and Taxonomy

Categorical Data Clustering: A Bibliometric Analysis and Taxonomy

A novel fuzzy clustering algorithm based on rough set and inhibitive factor

Fuzzy K-means clustering with fast density peak clustering on multivariate kernel estimator with evolutionary multimodal optimization clusters on a large dataset

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