Application of Bio-inspired Metaheuristics in the Data Clustering Problem

Application of Bio-inspired Metaheuristics in the Data Clustering Problem

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.

Saved in:
Bibliographic Details
Main Authors: Colanzi,Thelma Elita, Guez Assunção,Wesley Klewerton, Ramirez Pozo,Aurora Trinidad, B,Ana Cristina, Vendramin,Kochem, Barros Pereira,Diogo Augusto, Zorzo,Carlos Alberto, de Paula Filho,Pedro Luiz
Format: Digital revista
Language:English
Published: Centro Latinoamericano de Estudios en Informática 2011
Online Access:http://www.scielo.edu.uy/scielo.php?script=sci_arttext&pid=S0717-50002011000300006
Tags: Add Tag
No Tags, Be the first to tag this record!
Description
Summary: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.

Similar Items