Document clustering using particle swarm optimization

Fast and high-quality document clustering algorithms play an important role in effectively navigating, summarizing, and organizing information. Recent studies have shown that partitional clustering algorithms are more suitable for clustering large datasets. However, the K-means algorithm, the most c...

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Bibliographic Details
Main Authors: Cui, X., Potok, T.E., Palathingal, P.
Format: Conference Proceeding
Language:English
Subjects:
Clustering algorithms
Data mining
Hybrid power systems
Laboratories
Navigation
Organizing
Particle swarm optimization
Partitioning algorithms
Software algorithms
Software engineering
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Summary:Fast and high-quality document clustering algorithms play an important role in effectively navigating, summarizing, and organizing information. Recent studies have shown that partitional clustering algorithms are more suitable for clustering large datasets. However, the K-means algorithm, the most commonly used partitional clustering algorithm, can only generate a local optimal solution. In this paper, we present a particle swarm optimization (PSO) document clustering algorithm. Contrary to the localized searching of the K-means algorithm, the PSO clustering algorithm performs a globalized search in the entire solution space. In the experiments we conducted, we applied the PSO, K-means and hybrid PSO clustering algorithm on four different text document datasets. The number of documents in the datasets ranges from 204 to over 800, and the number of terms ranges from over 5000 to over 7000. The results illustrate that the hybrid PSO algorithm can generate more compact clustering results than the K-means algorithm.
DOI:10.1109/SIS.2005.1501621