A Novel Intra Centroid Based Clustering for Categorical Data of Documents

A Novel Intra Centroid Based Clustering for Categorical Data of Documents

  IJETT-book-cover  International Journal of Engineering Trends and Technology (IJETT)          
  
© 2017 by IJETT Journal
Volume-52 Number-3
Year of Publication : 2017
Authors : Ch.M.R.Veena, Dr.A.Chandra Sekhar

Citation 

Ch.M.R.Veena, Dr.A.Chandra Sekhar "A Novel Intra Centroid Based Clustering for Categorical Data of Documents", International Journal of Engineering Trends and Technology (IJETT), V52(3),175-178 October 2017. ISSN:2231-5381. www.ijettjournal.org. published by seventh sense research group

Abstract
Keyword extraction from documents is an interesting research issue in the field of knowledge and data engineering. Every fragment or statement contains collection of keywords and may have more than one occurrence or frequency in single snippet or document. Clustering is a mechanism which groups the similar type of objects based on the similarity between the objects. In this paper we propose an efficient keyword extraction based clustering model which groups the similar type of documents based on the similarity between the documents with cosine similarity and over novel centriod computational model improves the performance of the clusters. Our model improves the k means with elimination of random centroid selection, average pair wise distance and other parameters to generate consistent clusters our proposed model gives efficient results than traditional models.

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Keywords
In this paper we propose an efficient keyword extraction based clustering model which groups the similar type of documents based on the similarity between the documents with cosine similarity and over novel centriod computational model improves the performance of the clusters.