Multi-Objective Clustering Based on Congestion Games With Player-Specific Cost Functions
Cluster analysis, the unsupervised identification of homogeneous groups within data, has become essential in numerous domains due to its broad applicability. As a central task in unsupervised learning, clustering aims to uncover the underlying structure of data by grouping similar instances without...
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IEEE
2025-01-01
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| Online Access: | https://ieeexplore.ieee.org/document/11050432/ |
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| author | Dalila Kessira Mohand-Tahar Kechadi |
| author_facet | Dalila Kessira Mohand-Tahar Kechadi |
| author_sort | Dalila Kessira |
| collection | DOAJ |
| description | Cluster analysis, the unsupervised identification of homogeneous groups within data, has become essential in numerous domains due to its broad applicability. As a central task in unsupervised learning, clustering aims to uncover the underlying structure of data by grouping similar instances without relying on predefined labels. This paper proposes a multi-objective clustering technique based on concepts from game theory. In particular, we formulate the clustering problem using Singleton Congestion Games with Player-Specific Cost Functions, a sub-class of non-cooperative simultaneous-move games, in which cluster heads act as players and data objects are treated as resources. This clustering model is solved using the Nash equilibrium as the solution concept, which can be computed in polynomial time, thereby ensuring favourable computational complexity for the proposed algorithm, MOCA-II. Experimental results on diverse synthetic and real-world datasets demonstrate that MOCA-II outperforms both mono-objective and multi-objective state-of-the-art clustering methods in terms of clustering quality and efficiency. |
| format | Article |
| id | doaj-art-961cf37bc586451bba5fbb92a894b5e7 |
| institution | DOAJ |
| issn | 2169-3536 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | IEEE |
| record_format | Article |
| series | IEEE Access |
| spelling | doaj-art-961cf37bc586451bba5fbb92a894b5e72025-08-20T02:43:32ZengIEEEIEEE Access2169-35362025-01-011311255211256710.1109/ACCESS.2025.358324011050432Multi-Objective Clustering Based on Congestion Games With Player-Specific Cost FunctionsDalila Kessira0https://orcid.org/0000-0002-4022-5277Mohand-Tahar Kechadi1https://orcid.org/0000-0002-0176-6281LIMED Laboratory, Faculty of Exact Sciences, University of Bejaia, Bejaia, AlgeriaInsight Centre for Data Analytics, University College of Dublin, Dublin, IrelandCluster analysis, the unsupervised identification of homogeneous groups within data, has become essential in numerous domains due to its broad applicability. As a central task in unsupervised learning, clustering aims to uncover the underlying structure of data by grouping similar instances without relying on predefined labels. This paper proposes a multi-objective clustering technique based on concepts from game theory. In particular, we formulate the clustering problem using Singleton Congestion Games with Player-Specific Cost Functions, a sub-class of non-cooperative simultaneous-move games, in which cluster heads act as players and data objects are treated as resources. This clustering model is solved using the Nash equilibrium as the solution concept, which can be computed in polynomial time, thereby ensuring favourable computational complexity for the proposed algorithm, MOCA-II. Experimental results on diverse synthetic and real-world datasets demonstrate that MOCA-II outperforms both mono-objective and multi-objective state-of-the-art clustering methods in terms of clustering quality and efficiency.https://ieeexplore.ieee.org/document/11050432/Clusteringgame theorynon-cooperative gamescongestion games with player-specific cost functionsmulti-objectivenash equilibrium |
| spellingShingle | Dalila Kessira Mohand-Tahar Kechadi Multi-Objective Clustering Based on Congestion Games With Player-Specific Cost Functions IEEE Access Clustering game theory non-cooperative games congestion games with player-specific cost functions multi-objective nash equilibrium |
| title | Multi-Objective Clustering Based on Congestion Games With Player-Specific Cost Functions |
| title_full | Multi-Objective Clustering Based on Congestion Games With Player-Specific Cost Functions |
| title_fullStr | Multi-Objective Clustering Based on Congestion Games With Player-Specific Cost Functions |
| title_full_unstemmed | Multi-Objective Clustering Based on Congestion Games With Player-Specific Cost Functions |
| title_short | Multi-Objective Clustering Based on Congestion Games With Player-Specific Cost Functions |
| title_sort | multi objective clustering based on congestion games with player specific cost functions |
| topic | Clustering game theory non-cooperative games congestion games with player-specific cost functions multi-objective nash equilibrium |
| url | https://ieeexplore.ieee.org/document/11050432/ |
| work_keys_str_mv | AT dalilakessira multiobjectiveclusteringbasedoncongestiongameswithplayerspecificcostfunctions AT mohandtaharkechadi multiobjectiveclusteringbasedoncongestiongameswithplayerspecificcostfunctions |