Improving the K-Means Clustering Algorithm Oriented to Big Data Environments

Abstract

In recent years, the amount of texts in natural language, in digital format, has had an impressive increase. To obtain useful information from a large volume of data, new specialized techniques and efficient algorithms are required. Text mining consists of extracting meaningful patterns from texts; one of the basic approaches is clustering. The most used clustering algorithm is k-means. This chapter proposes an improvement of the k-means algorithm in the convergence step; the process stops whenever the number of objects that change their assigned cluster in the current iteration is bigger than the ones that changed in the previous iteration. Experimental results showed a reduction in execution time up to 93%. It is remarkable that, in general, better results are obtained when the volume of the text increase, particularly in those texts within big data environments.