计算机科学 ›› 2012, Vol. 39 ›› Issue (8): 196-198.
• 数据库与数据挖掘 • 上一篇 下一篇
邱 烨，何振峰
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摘要： 结合关联限制K-means算法能有效地提高聚类结果，但对数据对象分配次序却非常敏感。为获得一个好的分配次序，提出了一种基于分配次序聚类不稳定性的迭代习算法。根据Cop-Kmeans算法的稳定性特点，采用迭代思想，逐步确定数据对象的稳定性，进而确定分配次序。实验结果表明，基于分配次序聚类不稳定性迭代学习算法有效地提高了Cop-Kmcans算法的准确率。
关键词: 聚类分析，半监督聚类，K-means，关联限制
Abstract: Constrained K-means algorithm often improves clustering accuracy, but sensitive to the assignment order of instances. A clustering uncertainty based assignment order Iterative Learning Algorithm(UALA) was proposed to gain a good assignment order. The instances stability was gradually confirmed by iterative thought according to the characteristics of Cop-Kmeans algorithm stability, and then assignment order was confirmed. The experiment demonstrates that the algorithm effectively improves the accuracy of Cop-Kmeans algorithm.
Key words: Clustering analysis,Semi-supervise clustering,K-means,Instancelevel constraints
邱 烨，何振峰. 面向限制K-means算法的迭代学习分配次序策略[J]. 计算机科学, 2012, 39(8): 196-198. https://doi.org/
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