Big data and clustering algorithms

Data mining is the method which is useful for extracting useful information and data is extorted, but the classical data mining approaches cannot be directly used for big data due to their absolute complexity. The data that is been formed by numerous scientific applications and incorporated environment has grown rapidly not only in size but also in variety in recent era. The data collected is of very large amount and there is difficulty in collecting and assessing big data. Clustering algorithms have developed as a powerful meta learning tool which can precisely analyze the volume of data produced by modern applications. The main goal of clustering is to categorize data into clusters such that objects are grouped in the same cluster when they are “similar” according to similarities, traits and behavior. The most commonly used algorithm in clustering are partitioning, hierarchical, grid based, density based, and model based algorithms. A review of clustering and its different techniques in data mining is done considering the criteria's for big data. Where most commonly used and effective algorithms like K-Means, FCM, BIRCH, CLIQUE algorithms are studied and compared on big data perspective.