A Study of Different Partitioning Clustering Technique

In the field of software, Data mining is very useful to identify the interesting patterns and trends from the large amount of stored data into different database and data repository. Clustering technique is basically used to extract the unknown pattern from the large set of data for electronic stored data, business and real time applications. Clustering is a division of data into different groups. Data are grouped into clusters with high intra group similarity and low inter group similarity [2]. Clustering is an unsupervised learning technique. Clustering is useful technique that applied into many areas like marketing studies, DNA analysis, text mining and web documents classification. In the large database, the clustering task is very complex with many attributes. There are many methods to deal with these problems. In this paper we discuss about the different Partitioning Based Methods like- K-Means, K-Medoids and Fuzzy K-Means and compare the advantages or disadvantages over these techniques.