頁籤選單縮合
題 名 | An Efficient GA-Based Clustering Technique |
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作 者 | Lin, Hwei-jen; Yang, Fu-wen; Kao, Yang-ta; | 書刊名 | 淡江理工學刊 |
卷 期 | 8:2 民94.06 |
頁 次 | 頁113-122 |
分類號 | 494.542 |
關鍵詞 | Unsupervised clustering; Genetic algorithms; Reproduction; Crossover; Mutation; Fitness; Cluster validity; |
語 文 | 英文(English) |
英文摘要 | 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. |
本系統中英文摘要資訊取自各篇刊載內容。