A Survey of Some Density Based Clustering Techniques
This is an incremental survey paper that provides a comparative overview of existing methods for researchers in data mining.
The paper surveys various density-based clustering techniques, including DBSCAN and OPTICS, to analyze their characteristics and applicability for extracting patterns from different datasets.
Density Based Clustering are a type of Clustering methods using in data mining for extracting previously unknown patterns from data sets. There are a number of density based clustering methods such as DBSCAN, OPTICS, DENCLUE, VDBSCAN, DVBSCAN, DBCLASD and ST-DBSCAN. In this paper, a study of these methods is done along with their characteristics, advantages and disadvantages and most importantly, their applicability to different types of data sets to mine useful and appropriate patterns.