Implementation of K-Means Clustering in Big Data Environment - Gupta, Ayush; Gite, Pratik (International Journal of Computer Sciences and Engineering, 2019)
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.