Comparison of centroid-based clustering algorithms in the context of divide and conquer paradigm based FMST framework

Abstract

The practice of using divide and conquer techniques to solve complex, time-consuming problems has been in use for a very long time. Here we evaluate the performance of centroid-based clustering techniques, specifically k-means and its two approximation algorithms, the k-means++ and k-means|| (also known as Scalable k-means++), as divide and conquer paradigms applied for the creation of minimum spanning trees. The algorithms will be run on different datasets to get a good evaluation of their respective performances. This is a continuation of our previous work carried out in developing the KMST+ algorithm in the context of fast minimum spanning tree (FMST) frameworks.