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Published in:
2010 | OriginalPaper | Chapter
Effective Document Clustering with Particle Swarm Optimization
Authors : Ramanji Killani, K. Srinivasa Rao, Suresh Chandra Satapathy, Gunanidhi Pradhan, K. R. Chandran
Published in: Swarm, Evolutionary, and Memetic Computing
Publisher: Springer Berlin Heidelberg
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The paper presents a comparative analysis of K-means and PSO based clustering performances for text datasets. The dimensionality reduction techniques like Stop word removal, Brill’s tagger algorithm and mean Tf-Idf are used while reducing the size of dimension for clustering. The results reveal that PSO based approaches find better solution compared to K-means due to its ability to evaluate many cluster centroids simultaneously in any given time unlike K-means.