Clustering is the process for grouping of similar document into a single cluster and dissimilar documents in other clusters. Document clustering is the process of grouping similar text documents in a single cluster. K-means clustering algorithm is a center predictable approach which selects initial centers randomly. In this paper, improved k-means clustering algorithm is used for text documents which predicts centers manually. Standard k-means uses cosine similarity but improved k-means uses Euclidean similarity measures for grouping similar documents in a single cluster. According to experimental results, accuracy of improved k-means is high as compared to existing k-means algorithm. Performance of proposed algorithm is measured in terms of F-measure, Precision, time and recall.

Clustering, Document clustering, Tf-Idf, K-means, Euclidean similarity

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