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arXiv · 1406.4737

Performance Evaluation of Incremental K-means Clustering Algorithm

Sanjay Chakraborty·N. K. Nagwani
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Abstract

The incremental K-means clustering algorithm has already been proposed and analysed in paper [Chakraborty and Nagwani, 2011]. It is a very innovative approach which is applicable in periodically incremental environment and dealing with a bulk of updates. In this paper the performance evaluation is done for this incremental K-means clustering algorithm using air pollution database. This paper also describes the comparison on the performance evaluations between existing K-means clustering and incremental K-means clustering using that particular database. It also evaluates that the particular point of change in the database upto which incremental K-means clustering performs much better than the existing K-means clustering. That particular point of change in the database is known as "Threshold value" or "% delta change in the database". This paper also defines the basic methodology for the incremental K-means clustering algorithm.

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Sanjay Chakraborty, N. K. Nagwani. 2014-06-18. Performance Evaluation of Incremental K-means Clustering Algorithm. https://arxiv.org/abs/1406.4737

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