Metaheuristic Pattern Clustering – An Overview

Part of the Studies in Computational Intelligence book series (SCI, volume 178)


This chapter provides a comprehensive overview to the data clustering techniques, based on naturally-inspired metaheuristic algorithms. At first the clustering problem, similarity and dissimilarity measures between patterns and the methods of cluster validation are presented in a formal way. A few classical clustering algorithms are also addressed. The chapter then discusses the relevance of population-based approach with a focus on evolutionary computing in pattern clustering and outlines the most promising evolutionary clustering methods. The chapter ends with a discussion on the automatic clustering problem, which remains largely unsolved by most of the traditional clustering algorithms.


Particle Swarm Optimization Cluster Algorithm Cluster Center Fuzzy Cluster Cluster Problem 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.


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