A survey of cluster validity indices for automatic data clustering using differential evolution
| Autor: | Wilfrido Gómez-Flores, Adán José-García |
|---|---|
| Přispěvatelé: | Operational Research, Knowledge And Data (ORKAD), Centre de Recherche en Informatique, Signal et Automatique de Lille - UMR 9189 (CRIStAL), Centrale Lille-Université de Lille-Centre National de la Recherche Scientifique (CNRS)-Centrale Lille-Université de Lille-Centre National de la Recherche Scientifique (CNRS), Centro de Investigacion y de Estudios Avanzados del Instituto Politécnico Nacional (CINVESTAV), Université de Lille |
| Jazyk: | angličtina |
| Rok vydání: | 2021 |
| Předmět: |
Fitness function
Optimization problem Computer science Cohesion (computer science) 0102 computer and information sciences 02 engineering and technology computer.software_genre 01 natural sciences Silhouette [INFO.INFO-AI]Computer Science [cs]/Artificial Intelligence [cs.AI] [INFO.INFO-LG]Computer Science [cs]/Machine Learning [cs.LG] 010201 computation theory & mathematics Differential evolution 0202 electrical engineering electronic engineering information engineering Cluster (physics) 020201 artificial intelligence & image processing Data mining Cluster analysis computer Linear separability ComputingMilieux_MISCELLANEOUS |
| Zdroj: | GECCO '21: Genetic and Evolutionary Computation Conference GECCO '21: Genetic and Evolutionary Computation Conference, Jul 2021, Lille, France. pp.314-322, ⟨10.1145/3449639.3459341⟩ GECCO |
| DOI: | 10.1145/3449639.3459341⟩ |
| Popis: | International audience; In cluster analysis, the automatic clustering problem refers to the determination of both the appropriate number of clusters and the corresponding natural partitioning. This can be addressed as an optimization problem in which a cluster validity index (CVI) is used as a fitness function to evaluate the quality of potential solutions. Different CVIs have been proposed in the literature, aiming to identify adequate cluster solutions in terms of intracluster cohesion and intercluster separation. However, it is important to identify the scenarios in which these CVIs perform well and their limitations. This paper evaluates the effectiveness of 22 different CVIs used as fitness functions in an evolutionary clustering algorithm named ACDE based on differential evolution. Several synthetic datasets are considered: linearly separable data having both well-separated and overlapped clusters, and non-linearly separable data having arbitrarily-shaped clusters. Besides, real-life datasets are also considered. The experimental results indicate that the Silhouette index consistently reached an acceptable performance in linearly separable data. Furthermore, the indices Calinski-Harabasz, Davies-Bouldin, and generalized Dunn obtained an adequate clustering performance in synthetic and real-life datasets. Notably, all the evaluated CVIs performed poorly in clustering the non-linearly separable data because of the assumptions about data distributions. |
| Databáze: | OpenAIRE |
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