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基于成对约束的判别型半监督聚类分析   总被引:6,自引:1,他引:5       下载免费PDF全文
尹学松  胡恩良  陈松灿 《软件学报》2008,19(11):2791-2802
现有一些典型的半监督聚类方法一方面难以有效地解决成对约束的违反问题,另一方面未能同时处理高维数据.通过提出一种基于成对约束的判别型半监督聚类分析方法来同时解决上述问题.该方法有效地利用了监督信息集成数据降维和聚类,即在投影空间中使用基于成对约束的K均值算法对数据聚类,再利用聚类结果选择投影空间.同时,该算法降低了基于约束的半监督聚类算法的计算复杂度,并解决了聚类过程中成对约束的违反问题.在一组真实数据集上的实验结果表明,与现有相关半监督聚类算法相比,新方法不仅能够处理高维数据,还有效地提高了聚类性能.  相似文献
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Most existing representative works in semi-supervised clustering do not sufficiently solve the violation problem of pairwise constraints. On the other hand, traditional kernel methods for semi-supervised clustering not only face the problem of manually tuning the kernel parameters due to the fact that no sufficient supervision is provided, but also lack a measure that achieves better effectiveness of clustering. In this paper, we propose an adaptive Semi-supervised Clustering Kernel Method based on Metric learning (SCKMM) to mitigate the above problems. Specifically, we first construct an objective function from pairwise constraints to automatically estimate the parameter of the Gaussian kernel. Then, we use pairwise constraint-based K-means approach to solve the violation issue of constraints and to cluster the data. Furthermore, we introduce metric learning into nonlinear semi-supervised clustering to improve separability of the data for clustering. Finally, we perform clustering and metric learning simultaneously. Experimental results on a number of real-world data sets validate the effectiveness of the proposed method.  相似文献
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