Homogenous and Heterogenous Parallel Clustering: An Overview
It provides a comprehensive overview for researchers in data mining and machine learning, but it is incremental as it synthesizes existing work without introducing new methods.
This paper reviews parallel clustering algorithms, highlighting that a divide-and-conquer strategy often yields better results and improved time performance compared to centralized clustering.
Recent advances in computer architecture and networking opened the opportunity for parallelizing the clustering algorithms. This divide-and-conquer strategy often results in better results to centralized clustering with a much-improved time performance. This paper reviews key parallel clustering and provides insight into their strategy. The review brings together disparate attempts in parallel clustering to provide a comprehensive account of advances in this emerging field