題名: A New Density-Based Scheme for Clustering Based on Genetic Algorithm 作者: Lin, C. Y.;Chang, C. C.;Lin, C. C
關鍵詞: Clustering algorithms;genetic algorithms;DBSCAN 日期: 2005-08
上傳時間: 2009-12-17T06:58:33Z 出版者: Asia University
摘要: 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 algorithmto 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.
