Abstract
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.
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Killani, R., Rao, K.S., Satapathy, S.C., Pradhan, G., Chandran, K.R. (2010). Effective Document Clustering with Particle Swarm Optimization. In: Panigrahi, B.K., Das, S., Suganthan, P.N., Dash, S.S. (eds) Swarm, Evolutionary, and Memetic Computing. SEMCCO 2010. Lecture Notes in Computer Science, vol 6466. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-17563-3_73
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DOI: https://doi.org/10.1007/978-3-642-17563-3_73
Publisher Name: Springer, Berlin, Heidelberg
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