A Novel Intra Centroid Based Clustering for Categorical Data of Documents
|International Journal of Engineering Trends and Technology (IJETT)||
|© 2017 by IJETT Journal|
|Year of Publication : 2017|
|Authors : Ch.M.R.Veena, Dr.A.Chandra Sekhar
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
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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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.