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Large Scale Data Sets Clustering
Umesh Kumar and Ashish Mishra

The real-world big data can be clustered along desired dimensions but it is limited in its applicability to large-scale problems due to its high computational complexity, user’s desire, number of dimensions etc. Recently, many approaches have been proposed to accelerate the large-scale data clustering. Unfortunately, these methods usually sacrifice quite a lot of information of the original data; incompetent to produce multiple clustering etc and don’t consider the geometrical, psychological and physiological interpretation of naturally occurring data whose representation may be parts-based in human brain. In this paper eight clustering algorithms are analyzed which is based on large-scale data of eight different environment and dimensions to find out a universal framework for the representation and processing of knowledge. Our empirical study shows the encouraging results of the LSC-K algorithm in comparisons to state-of-the–art algorithms.

Keywords: Clustering, big data and analytics

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