Analysis and Study of Incremental K-Means Clustering Algorithm View Full Text


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Chapter Info

DATE

2011

AUTHORS

Sanjay Chakraborty , N. K. Nagwani

ABSTRACT

Study of this paper describes the incremental behaviours of partitioning based K-means clustering. This incremental clustering is designed using the cluster’s metadata captured from the K-Means results. Experimental studies shows that this clustering outperformed when the number of clusters increased, number of objects increased, length of the cluster radius decreased, while the incremental clustering outperformed when the number of new data objects are inserted into the existing database. In incremental approach, the K-means clustering algorithm is applied to a dynamic database where the data may be frequently updated. And this approach measure the new cluster centers by directly computes the new data from the means of the existing clusters instead of rerunning the K-means algorithm. Thus it describes, at what percent of delta change in the original database up to which incremental K-means clustering behaves better than actual K-means. It can be also used for large multidimensional dataset. More... »

PAGES

338-341

Book

TITLE

High Performance Architecture and Grid Computing

ISBN

978-3-642-22576-5
978-3-642-22577-2

Identifiers

URI

http://scigraph.springernature.com/pub.10.1007/978-3-642-22577-2_46

DOI

http://dx.doi.org/10.1007/978-3-642-22577-2_46

DIMENSIONS

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