A Survey of Data Mining Clustering Algorithms

Abstract

is a technique used in data mining that groups similar objects into one cluster, while dissimilar objects are grouped into different clusters. The clustering techniques can be categorized into partitioning methods, hierarchical methods, density-based methods and grid-based methods. The different partitioning methods studied here are k-means and k- medoids. The different hierarchical techniques studied here are BIRCH and CHAMELEON. The different grid-based techniques which are described are DBSCAN and DENCLUE. Lastly, the different techniques which are used in grid-based technique, like STING and CLIQUE are described. This paper aims to provide a brief overview and comparison of these different clustering algorithms and methods.