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Validation of Document Clustering based on Purity and Entropy measures
M.Deepa, P.Revathy PG Student, Dept. of CSE, Rajalakshmi Engineering College, Chennai, India Assistant Professor, Dept. of CSE, Rajalakshmi Engineering College, Chennai, India
Abstract: Document clustering aims to automatically group related document into clusters. If two documents are close to each other in the original document space, they are grouped into the same cluster. If the two documents are far away from each other in the original document space, they tend to be grouped into different cluster. The classical clustering algorithms assign each data to exactly one cluster, but fuzzy c-means allow data belong to different clusters. Fuzzy clustering is a powerful unsupervised method for the analysis of data. Cluster validity measure is useful in estimating the optimal number of clusters. Purity and Entropy are the validity measures used in this clustering.
Keywords: Document clustering, fuzzy c-means, validation, purity and entropy measures.
Keywords: Document clustering, fuzzy c-means, validation, purity and entropy measures.
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This work is licensed under a Creative Commons Attribution 4.0 International License.How to Cite:
[1] M.Deepa, P.Revathy PG Student, Dept. of CSE, Rajalakshmi Engineering College, Chennai, India Assistant Professor, Dept. of CSE, Rajalakshmi Engineering College, Chennai, India, βValidation of Document Clustering based on Purity and Entropy measures,β International Journal of Advanced Research in Computer and Communication Engineering (IJARCCE)