Novel validity indices for dynamic clustering and an Improved Dynamic Fuzzy C-Means
Dynamic clustering algorithms play a crucial role in numerous real-world applications by continuously adapting to evolving data patterns and identifying changes within the underlying cluster structure. However, unlike static clustering, where a plethora of validation indices exist to assess the solu...
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| Format: | Article |
| Language: | English |
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Elsevier
2025-03-01
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| Series: | Egyptian Informatics Journal |
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| Online Access: | http://www.sciencedirect.com/science/article/pii/S1110866525000052 |
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| author | Ramiro Saltos Ignacio Carvajal Fernando Crespo Richard Weber |
| author_facet | Ramiro Saltos Ignacio Carvajal Fernando Crespo Richard Weber |
| author_sort | Ramiro Saltos |
| collection | DOAJ |
| description | Dynamic clustering algorithms play a crucial role in numerous real-world applications by continuously adapting to evolving data patterns and identifying changes within the underlying cluster structure. However, unlike static clustering, where a plethora of validation indices exist to assess the solution’s quality, evaluating the effectiveness of dynamic clustering algorithms remains a challenge. This paper addresses this gap by proposing a novel set of six validation indices specifically designed for dynamic clustering. These indices assess the quality of solutions generated at three distinct granularities: individual clusters, individual observation periods, and the entire observation horizon. Our focus centers on cluster creation and elimination, recognized as the most critical structural changes within the dynamic clustering literature. To illustrate the application of these novel indices, we introduce an improved version of the dynamic fuzzy c-means algorithm (I-DFCM) which offers enhanced computational stability for handling dynamic data. We demonstrate the effectiveness of both the I-DFCM algorithm and the new validation indices through computational experiments using both synthetic and real-world datasets. The experiments showcase how these indices can effectively validate dynamic clustering solutions and guide parameter tuning for optimal performance, and support practical applications such as dynamic community detection in social networks and informed decision-making in dynamic environments. The results highlight the significant potential of these new validation indices and the I-DFCM algorithm in advancing the field of dynamic clustering. |
| format | Article |
| id | doaj-art-bbe99983137a46e1b41ee7ba884f4690 |
| institution | Kabale University |
| issn | 1110-8665 |
| language | English |
| publishDate | 2025-03-01 |
| publisher | Elsevier |
| record_format | Article |
| series | Egyptian Informatics Journal |
| spelling | doaj-art-bbe99983137a46e1b41ee7ba884f46902025-01-18T05:03:43ZengElsevierEgyptian Informatics Journal1110-86652025-03-0129100613Novel validity indices for dynamic clustering and an Improved Dynamic Fuzzy C-MeansRamiro Saltos0Ignacio Carvajal1Fernando Crespo2Richard Weber3Facultad de Administración y Economía, Universidad Diego Portales, Av. Santa Clara 797, Huechuraba, Santiago, Chile; Corresponding author.Department of Industrial Engineering, FCFM, Universidad de Chile, Av. Beauchef 851, Santiago de Chile, ChileDepartment of Management and Business, FEN, Universidad Alberto Hurtado, Erasmo Escala 1835, Santiago de Chile, ChileDepartment of Industrial Engineering, FCFM, Universidad de Chile, Av. Beauchef 851, Santiago de Chile, ChileDynamic clustering algorithms play a crucial role in numerous real-world applications by continuously adapting to evolving data patterns and identifying changes within the underlying cluster structure. However, unlike static clustering, where a plethora of validation indices exist to assess the solution’s quality, evaluating the effectiveness of dynamic clustering algorithms remains a challenge. This paper addresses this gap by proposing a novel set of six validation indices specifically designed for dynamic clustering. These indices assess the quality of solutions generated at three distinct granularities: individual clusters, individual observation periods, and the entire observation horizon. Our focus centers on cluster creation and elimination, recognized as the most critical structural changes within the dynamic clustering literature. To illustrate the application of these novel indices, we introduce an improved version of the dynamic fuzzy c-means algorithm (I-DFCM) which offers enhanced computational stability for handling dynamic data. We demonstrate the effectiveness of both the I-DFCM algorithm and the new validation indices through computational experiments using both synthetic and real-world datasets. The experiments showcase how these indices can effectively validate dynamic clustering solutions and guide parameter tuning for optimal performance, and support practical applications such as dynamic community detection in social networks and informed decision-making in dynamic environments. The results highlight the significant potential of these new validation indices and the I-DFCM algorithm in advancing the field of dynamic clustering.http://www.sciencedirect.com/science/article/pii/S1110866525000052Dynamic clusteringValidity indicesFuzzy C-Means |
| spellingShingle | Ramiro Saltos Ignacio Carvajal Fernando Crespo Richard Weber Novel validity indices for dynamic clustering and an Improved Dynamic Fuzzy C-Means Egyptian Informatics Journal Dynamic clustering Validity indices Fuzzy C-Means |
| title | Novel validity indices for dynamic clustering and an Improved Dynamic Fuzzy C-Means |
| title_full | Novel validity indices for dynamic clustering and an Improved Dynamic Fuzzy C-Means |
| title_fullStr | Novel validity indices for dynamic clustering and an Improved Dynamic Fuzzy C-Means |
| title_full_unstemmed | Novel validity indices for dynamic clustering and an Improved Dynamic Fuzzy C-Means |
| title_short | Novel validity indices for dynamic clustering and an Improved Dynamic Fuzzy C-Means |
| title_sort | novel validity indices for dynamic clustering and an improved dynamic fuzzy c means |
| topic | Dynamic clustering Validity indices Fuzzy C-Means |
| url | http://www.sciencedirect.com/science/article/pii/S1110866525000052 |
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