Automatic Clustering Based on Invasive Weed Optimization Algorithm

Lecture notes in computer sciencePublished 1 January 2011

Aritra Chowdhury, Sandip Bose, Swagatam Das

DOI10.1007/978-3-642-27242-4_13

Source URL

Citations27

SJR quartileQ2

SJR score0.35

SNIP0.55

TL;DR

An evolutionary metaheuristic algorithm known as the Invasive Weed Optimization (IWO) is applied for automatically partitioning a dataset without any prior information about the number of naturally occurring groups in the data.

Abstract

In this article, an evolutionary metaheuristic algorithm known as the Invasive Weed Optimization (IWO) is applied for automatically partitioning a dataset without any prior information about the number of naturally occurring groups in the data. The fitness function used in the genetic algorithm is a cluster validity index. Depending on the results of this index IWO returns the segmented dataset along with the appropriate number of divisions. The proficiency of this algorithm is compared to variable string length genetic algorithm with point symmetry based distance clustering(VGAPS-clustering), variable string length Genetic K-means algorithm(GCUK-clustering) and a weighted sum validity function based hybrid niching genetic algorithm(HNGA-clustering) and is denoted for the nine artificial datasets and four real life datasets.

Keywords

Computer Science

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