An Efficient GA-Based Clustering Technique
學年 93
學期 2
出版（發表）日期 2005-06-01
作品名稱 An Efficient GA-Based Clustering Technique
作品名稱（其他語言）
著者 林慧珍; Lin, Hwei-jen; Yang, Fu-wen; Kao, Yang-ta
單位 淡江大學資訊工程學系
出版者 淡江大學
著錄名稱、卷期、頁數 淡江理工學刊=Tamkang journal of science and engineering 8(2), pp.113-122
摘要 In this paper, we propose a GA-based unsupervised clustering technique that selects cluster centers directly from the data set, allowing it to speed up the fitness evaluation by constructing a look-up table in advance, saving the distances between all pairs of data points, and by using binary representation rather than string representation to encode a variable number of cluster centers. More effective versions of operators for reproduction, crossover, and mutation are introduced. Finally, the Davies-Bouldin index is employed to measure the validity of clusters. The development of our algorithm has demonstrated an ability to properly cluster a variety of data sets. The experimental results show that the proposed algorithm provides a more stable clustering performance in terms of number of clusters and clustering results. This results in considerable less computational time required, when compared to other GA-based clustering algorithms.
關鍵字 Unsupervised Clustering;Genetic Algorithms;Reproduction;Crossover;Mutation;Fitness;Cluster Validity
語言 en
ISSN 1560-6686
期刊性質 國內
收錄於 EI
產學合作
通訊作者
審稿制度 否
國別 TWN
公開徵稿
出版型式 ,電子版
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