In recent years the digital data is grown much frequently. Handling and processing of such bulky data are much complex and need the attention of a human. Moreover, the existing techniques and methods are not much suitable to deal with this complex nature of computation. To deal with such a complex nature of computation, the big data analytics played an essential role. In this presented work the unsupervised learning technique namely k-means clustering is implemented initially and their performance is measured. During this to enhance the performance of the system a new modified k-means clustering algorithm is proposed by improving the centroid selection technique and using the RBF kernel. The comparative performance analysis of both the versions of k-means clustering demonstrate the modified k-means clustering is efficient and has the low algorithm run time. Therefore it is a promising approach for analytics, thus it’s a future extension that is also presented in this work.

Big Data, Big Data Analytics, Unsupervised learning, Clustering Algorithm, improvements

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