Email this article An Approach to Text Documents Clustering with {n, n-1, ….., 1}-Word(s) Appearance Using Graph Mining Techniques
Abstract
This paper is about text document clustering with an input of n words. Initially a cluster of all text documents with extension name ".Txt" from m-documents of various types is formed. Then on an input of n-words, the proposed algorithm starts n, n-1, n-2,.....,1 sets of cluster. Each cluster of text documents with the presence of n, n-1, n-2,......,1 word(s) respectively. These n-forms of clustering are treated as documents-words relation and in memory it is represented as un-oriented documents-words incidence matrix. Finally these un-oriented documents-words incidence matrices are represented as bi-partite graphs, since the bi-partite graph has two sets of nodes namely document and word. The proposed algorithm using graph mining techniques was implemented using C++ programming language and the result was satisfactory.
References
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