Big Data Clustering: Applying Conventional Data Mining Techniques in Big Data Environment

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In the current data world, the term big data became popular term to define these massive data, increasing enormously. Efficient knowledge extraction techniques take major role in processing this big data. There are many techniques used to process big data; most importantly, the data mining technique–cluster analysis has many applications such as in information retrieval, image processing, machine learning, etc. and used widely. The clustering is the process of grouping similar data items into one class, dissimilar into another. The clustering of big data includes many overheads, particularly the designing new algorithms or conversion of efficient data mining algorithms for distributed environment. In this paper, we covered overview of traditional clustering techniques and trends in clustering models to process the big data, so that the current voluminous data can be processed and analyzed efficiently. Clustering of big data is the tremendous field of research in current trend that has huge scope for improvement of clustering algorithms.