At present time huge amount of useful data is available on web for access, and this huge amount of data is shared information which can be used by anyone intended to use. The availability of different types and nature of document data has lead to the task of clustering in large dataset. Clustering is one of the very important techniques used for classification of large dataset and widely applicable many areas. High-quality and fast document clustering algorithms play a significant role to successfully navigate, summarize and organize the information. Recent studies have shown that partitional clustering algorithms are suit- able for large datasets. The k-means algorithm [9, 10] is generally used as partitional clustering algorithm because it can be easily implemented and is most efficient in terms of execution time. The major problem with this algorithm is its sensitivity in selection of the initial partition and its convergence to local optima. In this research study we have refined the useful information from document data set using minimum spanning tree for document clustering and good quality of clusters have been generated on several document datasets, and the output show obtained indicates effective improvement in performance.

Minimum Spanning Tree, Document Clustering, World Wide Web, K-Means Algorithm

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