Review Paper on Clustering Techniques
The purpose of the data mining technique is to mine information from a bulky data set and make over it into a reasonable form for supplementary purpose. Clustering is a significant task in data analysis and data mining applications. It is the task of arrangement a set of objects so that objects in the identical group are more related to each other than to those in other groups (clusters). Data mining can do by passing through various phases. Mining can be done by using supervised and unsupervised learning. The clustering is unsupervised learning. A good clustering method will produce high superiority clusters with high intra-class similarity and low inter-class similarity. Clustering algorithms can be categorized into partition-based algorithms, hierarchical-based algorithms, density-based algorithms and grid-based algorithms. Partitioning clustering algorithm splits the data points into k partition, where each partition represents a cluster. Hierarchical clustering is a technique of clustering which divide the similar dataset by constructing a hierarchy of clusters. Density based algorithms find the cluster according to the regions which grow with high density. It is the one-scan algorithms. Grid Density based algorithm uses the multiresolution grid data structure and use dense grids to form clusters. Its main distinctiveness is the fastest processing time. In this survey paper, an analysis of clustering and its different techniques in data mining is done.
