A Survey for Hybrid Density-Based Data Clustering Algorithms

Abstract

The clustering algorithms importance has increased with the continuous increase of the data size collected from different sources every day. Clustering has been used in various applications for extracting and analyzing patterns from the data. However, data clustering is facing many challenges, which the researchers try to solve via different methods. In the paper, we present a study of a hybrid density-based combined with pother clustering methods for data clustering and other applications. We have analyzed some of the states of art methods and point out the strength and weaknesses points. Finally, we discuss some of the future research directions in this field.