Automatic generation of initial value k to apply k-means method for text documents clustering

Namita Gupta, P.C. Saxena and J.P. Gupta

International Journal of Data Mining, Modelling and Management, 2011, vol. 3, issue 1, 18-41

Abstract: Retrieving relevant text documents on a topic from a large document collection is a challenging task. Different clustering algorithms are developed to retrieve relevant documents of interest. Hierarchical clustering shows quadratic time complexity of O(n²) for n text documents. K-means algorithm has a time complexity of O(n) but it is sensitive to the initial randomly selected cluster centres, giving local optimum solution. Global k-means employs the k-means algorithm as a local search procedure to produce global optimum solution but shows polynomial time complexity of O(nk) to produce k clusters. In this paper, we propose an approach of clustering text documents that overcomes the drawback of k-means and global k-means and gives global optimal solution with time complexity of O(lk) to obtain k clusters from initial set of l starting clusters. Experimental evaluation on Reuters newsfeeds (Reuters-21578) shows clustering results (entropy, purity, F-measure) obtained by proposed method comparable with k-means and global k-means.

Keywords: document clustering; global k-means; information retrieval; data mining; text documents. (search for similar items in EconPapers)
Date: 2011
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