關鍵字查詢 | 類別:會議論文 | | 關鍵字:Robust Clustering based on Winner-Population Markov Chain

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序號 學年期 教師動態
1 95/1 資工系 楊富文 助理教授 會議論文 發佈 Robust Clustering based on Winner-Population Markov Chain , [95-1] :Robust Clustering based on Winner-Population Markov Chain會議論文Robust Clustering based on Winner-Population Markov ChainYang, Fu-wen; Lin, Hwei-jen; Wang, Patrick, S. P.; Wu, Hung-hsuan淡江大學資訊工程學系Proceedings of the 18th International Conference on Pattern Recognition (ICPR2006), pp.589-592IAPRIn this paper, we propose an unsupervised genetic clustering algorithm, which produces a new chromosome without any conventional genetic operators, and instead according to the gene reproducing probabilities determined by Markov chain modeling. Selection of cluster centers from the dataset enables construction of a look-up table that saves the distances between all pairs of data points. The experimental results show that the proposed algorithm not only solves the premature problem to provide a more stable clustering performance in terms of number of clusters and clustering results, but also improves the time efficiencytku_id: 000112405; 000086204;Made available in DSpace on 2010-01-11T04:30:56Z
2 95/1 資訊系 林慧珍 教授 會議論文 發佈 Robust Clustering based on Winner-Population Markov Chain , [95-1] :Robust Clustering based on Winner-Population Markov Chain會議論文Robust Clustering based on Winner-Population Markov ChainYang, Fu-wen; Lin, Hwei-jen; Wang, Patrick, S. P.; Wu, Hung-hsuan淡江大學資訊工程學系Proceedings of the 18th International Conference on Pattern Recognition (ICPR2006), pp.589-592IAPRIn this paper, we propose an unsupervised genetic clustering algorithm, which produces a new chromosome without any conventional genetic operators, and instead according to the gene reproducing probabilities determined by Markov chain modeling. Selection of cluster centers from the dataset enables construction of a look-up table that saves the distances between all pairs of data points. The experimental results show that the proposed algorithm not only solves the premature problem to provide a more stable clustering performance in terms of number of clusters and clustering results, but also improves the time efficiencytku_id: 000112405; 000086204;Made available in DSpace on 2010-01-11T04:30:56Z
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