A hybrid approach for data clustering based on modified cohort intelligence and K-means |
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| Affiliation: | 1. Department of Electrical Engineering, Faculty of Engineering, Universiti Malaya, Lembah Pantai, 50603 Kuala Lumpur, Malaysia;2. Odette School of Business, University of Windsor, 401 Sunset Ave, Windsor, ON N9B 3P4, Canada;1. College of Computer Science and Technology, Zhejiang University of Technology, Hangzhou 310023, China;2. School of Software Microelectronics, Peking University, Beijing 100190, China;1. University of Cauca, Cll. 5 4-70 Popayán, Colombia;2. Universidad Carlos III de Madrid, Av. Universidad 30, 28911 Leganés, Spain;3. University of East London, Docklands Campus, London E16 2RD, United Kingdom;1. Instituto de Engenharia Mecânica e Gestão Industrial, Faculdade de Engenharia, Universidade do Porto, R. Dr. Roberto Frias s/n, 4200-465 Porto, Portugal;2. Department of Urology, School of Medicine Stanford University, Stanford, CA, USA;1. Grup de Recerca en Sistemes Intel·ligents, Ramon Llull University, Quatre Camins 2, 08022 Barcelona, Spain;2. Grup de Recerca en Internet Technologies & Storage, Ramon Llull University, Quatre Camins 2, 08022 Barcelona, Spain;3. Departamento de Ingeniería Matemática e Informática, Universidad Pública de Navarra, Campus de Arrosadía, 31006 Pamplona, Spain |
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| Abstract: | Clustering is an important and popular technique in data mining. It partitions a set of objects in such a manner that objects in the same clusters are more similar to each another than objects in the different cluster according to certain predefined criteria. K-means is simple yet an efficient method used in data clustering. However, K-means has a tendency to converge to local optima and depends on initial value of cluster centers. In the past, many heuristic algorithms have been introduced to overcome this local optima problem. Nevertheless, these algorithms too suffer several short-comings. In this paper, we present an efficient hybrid evolutionary data clustering algorithm referred to as K-MCI, whereby, we combine K-means with modified cohort intelligence. Our proposed algorithm is tested on several standard data sets from UCI Machine Learning Repository and its performance is compared with other well-known algorithms such as K-means, K-means++, cohort intelligence (CI), modified cohort intelligence (MCI), genetic algorithm (GA), simulated annealing (SA), tabu search (TS), ant colony optimization (ACO), honey bee mating optimization (HBMO) and particle swarm optimization (PSO). The simulation results are very promising in the terms of quality of solution and convergence speed of algorithm. |
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| Keywords: | Clustering Cohort intelligence Meta-heuristic algorithm |
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