K-Means Performance Improvements With Centroid Calculation Heuristics Both for Serial and Parallel Environments

Abstract

k-means is the most widely used clustering algorithm due to its fairly straightforward implementations in various problems. Meanwhile, when the number of clusters increase, the number of iterations also tend to slightly increase. However there are still opportunities for improvement as some studies in the literature indicate. In this study, improved implementations of k-means algorithm with a centroid calculation heuristics which results in a performance improvement over traditional k-means are proposed. Two different versions of the algorithm for various data sizes are configured, one for small and the other one for big data implementations. Both the serial and MapReduce parallel implementations of the proposed algorithm are tested and analyzed using 2 different data sets with various number of clusters. The results show that big data implementation model outperforms the other compared methods after a certain threshold level and small data implementation performs better with increasing k value.