Performance Comparison of Incremental K-means and Incremental DBSCAN Algorithms

TL;DR

This paper compares the performance of incremental K-means and incremental DBSCAN clustering algorithms on dynamic data, highlighting their efficiency differences and characteristics using an air pollution dataset.

Contribution

It provides a performance evaluation and comparison of incremental K-means and DBSCAN algorithms on real-world data, elucidating their characteristics and differences.

Findings

Incremental algorithms are more efficient than their traditional counterparts.

Performance varies based on data change patterns.

Incremental DBSCAN and K-means have distinct characteristics in dynamic environments.

Abstract

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.