| 一种基于限制的PAM算法 |
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| 引用本文： | 何振峰. 一种基于限制的PAM算法[J]. 计算机工程与应用, 2006, 42(6): 190-192 |
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| 作者姓名： | 何振峰 |
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| 作者单位： | 福州大学数学与计算机学院计算机系,福州,350002 |
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| 摘 要： | 利用数据对象间的关联限制可以改善聚类算法的效果,但对于关联限制与K中心点算法的结合策略则少有研究。由此研究了关联限制与PAM算法的结合方法,提出了算法CPAM。首先基于限制找到一个合适的初始分隔;在接下来反复地调整中心点的过程中,也考虑到了所给限制。实验结果显示:CPAM可以有效地利用关联限制来提高一些实际数据集的准确率。
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| 关 键 词： | 聚类分析 PAM算法 半监督学习 |
| 文章编号： | 1002-8331-（2006）06-0190-03 |
| 收稿时间： | 2005-06-01 |
| 修稿时间： | 2005-06-01 |
A Constraint-based PAM Algorithm |
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| He Zhenfeng. A Constraint-based PAM Algorithm[J]. Computer Engineering and Applications, 2006, 42(6): 190-192 |
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| Authors: | He Zhenfeng |
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| Affiliation: | Computer Department,College of Mathematics and Computer Science, Fuzhou University,Fuzhou 350002 |
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| Abstract: | Instance-level constraints have shown to be useful to improve the performance of some existing clustering algorithms.There is yet little research on the approach of instance-level constraint-based K-median algorithm.An instance-level constralnt-based PAM algorithm CPAM is presented.h begins by finding an initial partition that is complying with the constraints.Then it does the replacement of a medoid by a nonmedoid iteratively,at all the time constraints will be taken into consideration.The test on three real datasets suggests that CPAM is effective in utilizing instance-level constraints in clustering some real datasets. |
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| Keywords: | clustering analysis PAM algorithm semi-supervised learning |
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