A Comparative Study of Various Clustering Algorithms in Data Mining
The purpose of the data mining technique is to mine information from a bulky data set and make it into a reasonable form for supplementary purpose. Data
mining can do by passing through various phases. Mining can be done by using supervised and unsupervised learning. Clustering is a significant task in data analysis and data mining applications. It is the task of arranging a set of objects so that objects in the identical group are more related to each other than to those in other groups (clusters). The clustering is unsupervised learning. Clustering algorithms can be classified into partition-based algorithms, hierarchical based algorithms, density-based algorithms and grid-based algorithms. This paper focuses on a keen study of different clustering algorithms in data mining .