Document Clustering Through Non-Negative Matrix Factorization: A Case Study of Hadoop for Computational Time Reduction of Large Scale Documents


In this paper we discuss a new model for document clustering which has been adapted using non-negative matrix factorization method. The key idea is to cluster the documents after measuring the proximity of the documents with the extracted features. The extracted features are considered as the final cluster labels and clustering is done using cosine… (More)


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