A Survey on Different Clustering Algorithms with Their Major Features
Data mining techniques make it possible to search large amounts of data for characteristic rules and patterns. Clustering is used to organize data for efficient retrieval. The aim is to create homogeneous subgroups of examples. The individuals in the same subgroup are similar; the individuals in different subgroups are as different as possible. One of the problems in clustering is the identification of clusters in given data. A popular technique for clustering is based on K-means such that the data is partitioned into K clusters. K‐means is a clustering (unsupervised learning) algorithm. In this method, the number of clusters is pre defined and the technique is highly dependent on the initial identification of elements that represent the clusters well. But we cannot change number of cluster at mid of execution of algorithm. But in k-mean, important factor is that how many clusters we should take, it may be less and it may be more. This paper gives an overview of different clustering algorithms used in large data sets. It describes about the general working behaviour, and the methodologies followed on these approaches and the parameters which used in these algorithms with large data sets.
Keywords: Data-mining, association, clustering
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