Clustering Based on Sequential Multi-Objective Games

Heloulou, Imen; Radjef, Mohammed Saïd; Kechadi, Tahar

doi:10.1007/978-3-319-10160-6_33

Cited by 2 publications

(3 citation statements)

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“…Hariz & Elouedi (2014) BCDP: IKBKM and DKBKM [111] dynamic clustering based on the K-modes algorithm that uses the Transferable Belief Model (TBM) concepts [112] BKM [113] Cao et al ( 2017) k-mw-modes [68] clustering categorical matrix-object data based on the K-modes algorithm K-modes, Wk-modes [114], Cao [115], FCCM [116] Heloulou et al ( 2017) MOCSG [81] the multi-objective clustering based-sequential game theoretic that extends the ClusSMOG algorithm [117] K-modes, PAM [118], and single linkage algorithm [16] Salem et al ( 2018) MFk-M [64] frequency-based method to update the modes and the Manhattan distance metric to compute the distance K-modes, K-means…”

Section: Authors (Year) Algorithms Methods Comparisonsmentioning

confidence: 99%

“…Optimizing the objective function based on a multi-objective approach Another method related to multi-objective clustering based on sequential games is the MOCSG [81]. Inspired by their previous work, clustering based on sequential multiobjective games (CluSMOG) [117], MOCSG extends this approach to numerical data. As a multi-objective clustering algorithm, MOCSG integrates multiple objective functions to optimize R-square (RSQ), connectivity, and intra-cluster inertia objectives.…”

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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: Authors (Year) Algorithms Methods Comparisonsmentioning

confidence: 99%

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confidence: 99%

Categorical Data Clustering: A Bibliometric Analysis and Taxonomy

Cendana,

Kuo

2024

MAKE

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“…Another work which proposed game theory for clustering is Li et al (2010) in which automatic formation of clusters is considered. Imen, Radjef, and Kechadi (2014) introduced a clustering method with the help of multiobjective games. The number of clusters is calculated automatically.…”

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confidence: 99%

A geodesic distance‐based approach for shape‐independent data clustering using coalitional game

2018

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Different clustering approaches consider different aspects of quality including accuracy of cluster formation, speed of execution, and resource requirements. A major issue in data clustering is to consider the density of clusters and their structure. A challenge is to relax any assumption on the clusters shape such as sphericity, which is common in most partitioning approaches such as K‐means. In this article, with the help of the coalitional game theory and the concept of geodesic distance calculation, a density‐based shape‐independent clustering approach is proposed. In addition to the emphasis on the application of game theory, we also pay attention to the relative neighbourhood of data, which is depicted using geodesic distance. Geodesic distance is a well‐known measure for finding the manifold of data. The new idea supposes to discover any embedded structure of data and avoid finding only spherical clusters. The proposed approach is evaluated on a number of standard University of California, Irvine (UCI) datasets, and the results show the effectiveness of the proposed approach in comparison with some other approaches.

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Clustering Based on Sequential Multi-Objective Games

Cited by 2 publications

References 12 publications

Categorical Data Clustering: A Bibliometric Analysis and Taxonomy

Categorical Data Clustering: A Bibliometric Analysis and Taxonomy

A geodesic distance‐based approach for shape‐independent data clustering using coalitional game

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