Analysis the Issues and Clustering Techniques of Data Mining

A Comparative Analysis of Clustering Algorithms for Data Mining

Abstract

Data mining has been utilized as a method to address the situation. Data mining, known to be a step in the process of knowledge exploration in databases, is a method of removing hidden information from large collections of databases in order to dig up eloquent trends and laws. Data mining has been an important part of nearly every area of human life. Clustering is a method in which a given data set is separated into groups called clusters in such a manner that similar data points are grouped together in a single cluster. Due to the large number of data sets, clustering plays an important role in data mining. This review analyzes the available literature on data mining, clustering algorithms used for data mining and presents a comparative analysis of different clustering algorithms such as DBSCAN, CLARA, CURE, CLARANS, KMeans, etc. Several implementations, tasks and related issues have also been highlighted.