  Incremental K-means and DBSCAN are two very important and popular clustering
techniques for today's large dynamic databases (Data warehouses, WWW and so on)
where data are changed at random fashion. The performance of the incremental
K-means and the incremental DBSCAN are different with each other based on their
time analysis characteristics. Both algorithms are efficient compare to their
existing algorithms with respect to time, cost and effort. In this paper, the
performance evaluation of incremental DBSCAN clustering algorithm is
implemented and most importantly it is compared with the performance of
incremental K-means clustering algorithm and it also explains the
characteristics of these two algorithms based on the changes of the data in the
database. This paper also explains some logical differences between these two
most popular clustering algorithms. This paper uses an air pollution database
as original database on which the experiment is performed.
