A rough set based algorithm for updating the modes in categorical clustering

Salem, Semeh Ben; Naouali, Sami; Chtourou, Zied

doi:10.1007/s13042-021-01293-w

Cited by 8 publications

(5 citation statements)

References 43 publications

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“…Similar to J. Y. Liang, R. J. Kuo has conducted studies on metaheuristic-based clustering in collaboration with T. P. Q. Nguyen [2,[53][54][55]60,69]. Other authors, such as S. Salem, S. Naouali, and Z. Chtourou, have also worked on rough-set clustering [64,[70][71][72]. Furthermore, Y. M. Cheung, as the second author, has proposed numerous methods related to distance metrics with Y. Q. Zhang [73][74][75] and H. Jia [58].…”

Section: • Publication Titles and Publishersmentioning

confidence: 98%

“…Another algorithm, known as the density rough K-modes (DRk-M) algorithm [70][71][72], has been proposed to address the issue of random selection during the update of modes in the K-Modes algorithm. The DRk-M algorithm calculates the density of the modes and subsequently applies RST to select the most suitable modes based on the concepts of lower and upper approximations in RST.…”

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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 Titles and Publishersmentioning

confidence: 98%

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confidence: 99%

Categorical Data Clustering: A Bibliometric Analysis and Taxonomy

Cendana,

Kuo

2024

MAKE

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“…However, in the teaching evaluation of loose-leaf textbooks, there is a certain correlation between different attributes, and the K-modes algorithm needs to be optimised. The study introduces the sum of error squares to determine the number of clustering families, as shown in equation (5).…”

Section: Teaching Evaluation Technique Of Loose-leaf Textbook Based O...mentioning

confidence: 99%

“…How to determine the weights of different indicators to comprehensively evaluate the use of loose-leaf textbooks is also a complex issue. Moreover, in the multidimensional evaluation of the use of loose-leaf textbooks, it is necessary to take into account the student feedback, teacher feedback and other subjective factors, which are often fuzzy, and need to be converted to data that can be calculated by the system through technical means in the multidimensional evaluation [5] . The K-modes algorithm is a clustering algorithm for discrete data, which can be used to classify the data of students or textbooks or teachers into different categories.…”

Section: Introductionmentioning

confidence: 99%

A study on multidimensional evaluation of teaching using loose-leaf textbooks based on improved K-modes algorithm

Tang

2024

Fourth International Conference on Computer Vision and Data Mining (ICCVDM 2023)

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Loose-leaf textbook is a new type of textbook in the new era of education environment, which has better interactivity and flexibility of use than traditional textbooks. In order to improve effect analysis of teaching with loose-leaf textbooks, a multi-dimensional evaluation method of teaching with loose-leaf textbooks based on the improved K-modes algorithm is designed. In the process, the multi-dimensional evaluation system of teaching using loose-leaf textbooks is constructed by combining objective and subjective directions, and the corresponding index value calculation method is designed, and then the data processing of multi-dimensional evaluation based on the improved K-modes algorithm is optimised by introducing the error sum of squares and the co-presence rate to optimise the clustering process, so as to obtain a complete multi-dimensional evaluation method. Results show that the research method maintains the accuracy rate above81.2% when the number of sample points increases to 1200 when performing data classification accuracy analysis; when generating the multidimensional evaluation results, the research method generates realistic evaluation results for 8indicators. It shows that the research method can effectively carry out the multidimensional evaluation of teaching using loose-leaf textbooks, and can provide certain technical references to the education industry.

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“…Rough set theory, proposed by Z. Pawlak in 1982, is a new mathematical tool to deal with quantitative uncertainty, vagueness, and imprecision information 1,2 . It also has the ability of deduction, 3 reduction, 4 and common sense reasoning 5,6 . In the past decades, rough set theory has been widely applied in the fields of machine learning, knowledge acquisition, decision analysis, knowledge discovery in databases, expert system, and pattern recognition 7 …”

Section: Introductionmentioning

confidence: 99%

Formalizing rough sets using a new noncontingency axiomatic system

Deng

Guan

Wang

et al. 2021

Int J of Intelligent Sys

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Formalization of rough sets is a key issue in rough set theory. When rough sets are formalized by propositional logic, predicate logic, or modal propositional logic, it easily suffers from some problems. For instance, an incomplete system is obtained. The concepts of “ p r e c i s e” or “ r o u g h” of rough sets cannot be described. To tackle these issues, a new noncontingency axiomatic system is proposed for formalizing rough sets in this paper. First, a new concise accessibility relation is defined for the axiomatic system; then, two simpler axiom schemas of the axiomatic system are designed to replace the axiom schema K. This is helpful to prove the soundness and completeness theorems for the axiomatic system. Finally, rough sets can be perfectly formalized by our proposed axiomatic system. Theoretical analysis proves that a complete formal system is achieved. In addition, the concepts of “ p r e c i s e” or “ r o u g h” of rough sets can be described without the help of semantics functions of metalanguage.

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A rough set based algorithm for updating the modes in categorical clustering

Cited by 8 publications

References 43 publications

Categorical Data Clustering: A Bibliometric Analysis and Taxonomy

Categorical Data Clustering: A Bibliometric Analysis and Taxonomy

A study on multidimensional evaluation of teaching using loose-leaf textbooks based on improved K-modes algorithm

Formalizing rough sets using a new noncontingency axiomatic system

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