| 摘要: |
| 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. |
| 关键词: 半监督聚类 成对约束 闭包中心 投影矩阵 聚类分析 |
| DOI： |
| 分类号: |
| 基金项目:Supported by the National Natural Science Foundation of China under Grant Nos.60505004, 60773061 (国家自然科学基金) |
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| Discriminative Semi-Supervised Clustering Analysis with Pairwise Constraints |
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YIN Xue-Song,HU En-Liang,CHEN Song-Can
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| Abstract: |
| Most existing semi-supervised clustering algorithms with pairwise constraints neither solve the problem of violation of pairwise constraints effectively, nor handle the high-dimensional data simultaneously. This paper presents a discriminative semi-supervised clustering analysis algorithm with pairwise constraints, called DSCA, which effectively utilizes supervised information to integrate dimensionality reduction and clustering. The proposed algorithm projects the data onto a low-dimensional manifold, where pairwise constraints based K-means algorithm is simultaneously used to cluster the data. Meanwhile, pairwise constraints based K-means algorithm presented in this paper reduces the computational complexity of constraints based semi-supervised algorithm and resolve the problem of violating pairwise constraints in the existing semi-supervised clustering algorithms. Experimental results on real-world datasets demonstrate that the proposed algorithm can effectively deal with high-dimensional data and provide an appealing clustering performance compared with the state-of-the-art semi-supervised algorithm. |
| Key words: semi-supervised clustering pairwise constraints closure centroid projection matrix clustering analysis |
