N-Clustering of Text Documents Using Graph Mining Techniques

The chapter is about the clustering of text documents based on the input of the n-number of words on the m-number of text documents using graph mining techniques. The author has proposed an algorithm for clustering of text documents by inputting n-number of words on m-number of text documents. First of all the proposed algorithm starts the selection of documents with extension name “.txt” from m-numbers of documents having various types of extension names. The n-number of words are input on the selected “.txt” documents, the algorithm starts n-clustering of text documents based on an n-input word. This is possible by way of creation of a document-word frequency matrix in the memory. Then the frequency-word table is converted into the un-oriented document-word incidence matrix by replacing all non-zeros with 1s. Using the un-oriented document-word incidence matrix, the algorithm starts the creation of n-number of clusters of text documents having the presence of words ranging from 1 to n respectively. Finally, these n-clusters based on word-wise as well as 1 to n word-wise.