A New Density-Based Scheme for Clustering Based on Genetic Algorithm

• Autorzy: Lin C.-Y. , Chang C.-C. , Lin C.-C.
• Języki publikacji: EN

Abstrakty

EN

Density-based clustering can identify arbitrary data shapes and noises. Achieving good clustering performance necessitates regulating the appropriate parameters in the density-based clustering. To select suitable parameters successfully, this study proposes an interactive idea called GADAC to choose suitable parameters and accept the diverse radii for clustering. Adopting the diverse radii is the original idea employed to the density-based clustering, where the radii can be adjusted by the genetic algorithm to cover the clusters more accurately. Experimental results demonstrate that the noise and all clusters in any data shapes can be identified precisely in the proposed scheme. Additionally, the shape covering in the proposed scheme is more accurate than that in DBSCAN.

Słowa kluczowe

EN

clustering algorithms; genetic algorithm; DBSCAN

• Wydawca: IOS Press
• Czasopismo: Fundamenta Informaticae
• Rocznik: 2005
• Tom: Vol. 68, nr 4
• Strony: 315--331
• Opis fizyczny: Bibliogr. 18 poz., tab., wykr.

Twórcy

autor

Lin C.-Y.

• Department of Computer Science and Information Engineering, National Chung Cheng Uniersity, Chiayi, Taiwan, 621, R.O.C.

autor

Chang C.-C.

• Department of Computer Science and Information Engineering, National Chung Cheng Uniersity, Chiayi, Taiwan, 621, R.O.C.

autor

Lin C.-C.

• Department of Computer Science and Information Management , London Providence, University Taichung, Taiwan 433, R.O.C.

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