A PSO-Based Subtractive Data Clustering Algorithm

There is a tremendous proliferation in the amount of information available on the largest shared information source, the World Wide Web. Fast and high-quality clustering algorithms play an important role in helping users to effectively navigate, summarize, and organize the information. Recent studie...

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Bibliographic Details
Published in:International journal of research in computer science 2013-03, Vol.3 (2), p.1-9
Main Authors: El-Tarabily, Mariam, Abdel-Kader, Rehab, Marie, Mahmoud, Abdel-Azeem, Gamal
Format: Article
Language:English
Subjects:
Algorithms
Clustering
Clusters
Computer simulation
Estimates
Identification methods
Mathematical models
Optimization
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Summary:There is a tremendous proliferation in the amount of information available on the largest shared information source, the World Wide Web. Fast and high-quality clustering algorithms play an important role in helping users to effectively navigate, summarize, and organize the information. Recent studies have shown that partitional clustering algorithms such as the k-means algorithm are the most popular algorithms for clustering large datasets. The major problem with partitional clustering algorithms is that they are sensitive to the selection of the initial partitions and are prone to premature converge to local optima. Subtractive clustering is a fast, one-pass algorithm for estimating the number of clusters and cluster centers for any given set of data. The cluster estimates can be used to initialize iterative optimization-based clustering methods and model identification methods. In this paper, we present a hybrid Particle Swarm Optimization, Subtractive + (PSO) clustering algorithm that performs fast clustering. For comparison purpose, we applied the Subtractive + (PSO) clustering algorithm, PSO, and the Subtractive clustering algorithms on three different datasets. The results illustrate that the Subtractive + (PSO) clustering algorithm can generate the most compact clustering results as compared to other algorithms. [PUBLICATION ABSTRACT]
ISSN:2249-8257
2249-8265
DOI:10.7815/ijorcs.32.2013.060