HeMI ++: A Genetic Algorithm based Clustering Technique for Sensible Clusters
| Autor: | A. H. Beg, Zahidul Islam, Vladimir Estivill-Castro |
|---|---|
| Rok vydání: | 2020 |
| Předmět: |
education.field_of_study
business.industry Complexity theory Statistics Population Biological cells Indexes Pattern recognition 02 engineering and technology Genetic algorithms User input Tree (data structure) Sociology 020204 information systems Metric (mathematics) Genetic algorithm 0202 electrical engineering electronic engineering information engineering 020201 artificial intelligence & image processing Artificial intelligence education business Cluster analysis Cloning |
| Zdroj: | CEC Charles Sturt University |
| DOI: | 10.1109/cec48606.2020.9185882 |
| Popis: | Comunicació presentada al IEEE Congress on Evolutionary Computation (CEC 2020), celebrat del 19 al 24 de juliol de 2020 a Glasgow, Escòcia. 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. |
| Databáze: | OpenAIRE |
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