A Novel Split-Merge-Evolve k Clustering Algorithm
Mingxue Wang, Vincent Huang, Anne-Marie Bosneag · 2018
Clustering algorithms are used in a large number of big data analytic applications spread across various application domains, including network management. We propose a novel Split-merge-evolve algorithm for clustering data into k number of clusters. The algorithm randomly divides data into k clusters initially, then repeatedly splits bad clusters and merges closest clusters to evolve the final clustering result. A key metric during the clustering process of the Split-merge-evolve algorithm is a user chosen or defined clustering quality metric or internal evaluation. The algorithm evolves the clustering result towards the user expected high quality result, although there is no ground truth or labelled data involved during the clustering process. The algorithm design is flexible in its implementation, with various common techniques, such as centroid and connectivity based measures that can be used in its implementation. The algorithm is easy to implement and effective. With 4 datasets, including 2 real life datasets in our experiments, the Split-merge-evolve algorithm performs better than both most commonly used K-means and Agglomerative hierarchical algorithms.