Parallel K-Means Algorithm for Shared Memory Multiprocessors

Clustering is the task of assigning a set of instances into groups in such a way that is dissimilarity of instances within each group is minimized. Clustering is widely used in several areas such as data mining, pattern recognition, machine learning, image processing, computer vision and etc. K-means is a popular clustering algorithm which partitions instances into a fixed number clusters in an iterative fashion. Although k-means is considered to be a poor clustering algorithm in terms of result quality, due to its simplicity, speed on practical applications, and iterative nature it is selected as one of the top 10 algorithms in data mining [1]. Parallelization of k-means is also studied during the last 2 decades. Most of these work concentrate on shared-nothing architectures. With the advent of current technological advances on GPU technology, implementation of the k-means algorithm on shared memory architectures recently start to attract some attention. However, to the best of our knowledge, no in-depth analysis on the performance of k-means on shared memory multiprocessors is done in the literature. In this work, our aim is to fill this gap by providing theoretical analysis on the performance of k-means algorithm and presenting extensive tests on a shared memory architecture.