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.