  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.
