An interval weighed fuzzy c-means clustering by genetically guided alternating optimization |
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Affiliation: | 1. School of Control Science and Engineering, Dalian University of Technology, Dalian 116024, China;2. Department of Electrical and Computer Engineering, University of Alberta, Edmonton T6R 2V4 AB, Canada;3. School of Information, Liaoning University, Shenyang 110036, China;1. Faculty of Electronic Engineering, University of Niš, Aleksandra Medvedeva 14, Niš, Serbia;2. Faculty of Mechanical Engineering, University of Niš, Aleksandra Medvedeva 14, Niš, Serbia;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;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 |
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Abstract: | The fuzzy c-means (FCM) algorithm is a widely applied clustering technique, but the implicit assumption that each attribute of the object data has equal importance affects the clustering performance. At present, attribute weighted fuzzy clustering has became a very active area of research, and numerous approaches that develop numerical weights have been combined into fuzzy clustering. In this paper, interval number is introduced for attribute weighting in the weighted fuzzy c-means (WFCM) clustering, and it is illustrated that interval weighting can obtain appropriate weights more easily from the viewpoint of geometric probability. Moreover, a genetic heuristic strategy for attribute weight searching is proposed to guide the alternating optimization (AO) of WFCM, and improved attribute weights in interval-constrained ranges and reasonable data partition can be obtained simultaneously. The experimental results demonstrate that the proposed algorithm is superior in clustering performance. It reveals that the interval weighted clustering can act as an optimization operator on the basis of the traditional numerical weighted clustering, and the effects of interval weight perturbation on clustering performance can be decreased. |
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Keywords: | Fuzzy clustering Attribute weighting Interval number Genetic algorithm Alternating optimization |
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