HeMI ++: a genetic algorithm based clustering technique for sensible clusters

Abstract

We propose a new clustering technique called HeMI++. It uses cleansing and cloning operations that help to produce sensible clusters. HeMI++ learns necessary properties of a good clustering solution for a dataset from a high-quality initial population, without requiring any user input. It then disqualifies the chromosomes that do not satisfy the properties through its cleansing operation. In the cloning operation, HeMI++ replaces the chromosomes by high-quality chromosomes already found in the initial population. We compare HeMI++ with six (6) existing techniques on twenty (20) publicly available datasets using the Tree Index metric. Our experimental results indicate a clear superiority of HeMI++ over existing methods. We also apply HeMI++ on a brain dataset and demonstrate its ability to produce sensible clusters.