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International Journal of Advanced Computer Research (IJACR)

ISSN (Print):2249-7277    ISSN (Online):2277-7970
Volume-3 Issue-12 September-2013
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Paper Title : Comparative Study of K-means and Robust Clustering
Author Name : Shashi Sharma, Ram Lal Yadav
Abstract :

Data mining is the mechanism of implementing patterns in large amount of data sets involving methods at the intersection of artificial intelligence, machine learning, statistics, and database systems. Clustering is the very big area in which grouping of same type of objects in data mining. Clustering has divided into different categories – partitioned clustering and hierarchical clustering. In this paper we study two types of clustering first is Kmeans which is part of partitioned clustering. Kmeans clustering generates a specific number of disjoint, flat (non-hierarchical) clusters. Second clustering is robust clustering which is part of hierarchical clustering. This clustering uses Jaccard coefficient instead of using the distance measures to find the similarity between the data or documents to classify the clusters. We show comparison between Kmeans clustering and robust clustering which is better for categorical data.

Keywords : Data mining, clustering, Kmeans, Robust, Partitioned, Hierarchical, Jaccard coefficient, analysis.
Cite this article : Shashi Sharma, Ram Lal Yadav, " Comparative Study of K-means and Robust Clustering " , International Journal of Advanced Computer Research (IJACR), Volume-3, Issue-12, September-2013 ,pp.207-210.