A PSO-Based Subtractive Data Clustering Algorithm

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Author(s):
Mariam El-Tarabily, Rehab Abdel-Kader, Mahmoud Marie, Gamal Abdel-Azeem
Published Date:
March 05, 2013
Issue:
Volume 3, Issue 2
Page(s):
1 - 9
DOI:
10.7815/ijorcs.32.2013.060
Views:
3691
Downloads:
155

Keywords:
data clustering, subtractive clustering, particle swarm optimization, subtractive algorithm, hybrid algorithm
Citation:
Mariam El-Tarabily, Rehab Abdel-Kader, Mahmoud Marie, Gamal Abdel-Azeem, "A PSO-Based Subtractive Data Clustering Algorithm". International Journal of Research in Computer Science, 3 (2): pp. 1-9, March 2013. doi:10.7815/ijorcs.32.2013.060 Other Formats

Abstract

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.

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