Clustering analysis for large scale data sets

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 seven clustering algorithms are analyzed which is based on large scale data of seven 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.