Abstract

 
 
 Clustering analysis includes a number of different algorithms and methods for grouping objects by their similar characteristics into categories. In recent years, considerable effort has been made to improve such algorithms performance. In this sense, this paper explores three different bio-inspired metaheuristics in the clustering problem: Genetic Algorithms (GAs), Ant Colony Optimization (ACO), and Artificial Immune Systems (AIS). This paper proposes some refinements to be applied to these metaheuristics in order to improve their performance in the data clustering problem. The performance of the proposed algorithms is compared on five different numeric UCI databases. The results show that GA, ACO and AIS based algorithms are able to efficiently and automatically forming natural groups from a pre-defined number of clusters.
 
 
Highlights
Many researchers from different areas face a general question of how to organize observed data into meaningful structures, that is, to develop taxonomies; this question is answered by clustering analysis
This paper analyzed the performance of the Genetic Algorithms (GAs), Ant Colony Optimization (ACO) and Artificial Immune Systems (AIS) metaheuristics for solving data clustering problem in an experiment with five numeric databases
Experimental results provided evidences that GA, ACO and AIS are suitable metaheuristics to deal with this problem in the context of our experiment
Summary
Many researchers from different areas face a general question of how to organize observed data into meaningful structures, that is, to develop taxonomies; this question is answered by clustering analysis. Each point is classified using Euclidean distance metric to the nearest center Metaheuristics, such as Genetic Algorithms (GA), Ant Colony Optimization (ACO), Artificial Bee Colony (ABC) algorithm, and Artificial Immune System have been efficiently used to achieve optimal or approximately optimal solutions without requiring prior knowledge about the data set to be clustered [3][4][5][6][7][8]. AIS cover algorithms inspired by the principles and processes of the vertebrate immune system These algorithms represent a new approach and its application to clustering deserves to be studied. GA and ACO algorithms and their refinements through local search, and AIS algorithms were developed to solve clustering problem.
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