用树结构建模定性数据的内在顺序，提升聚类准确率
Learning Order Forest for Qualitative-Attribute Data Clustering
- 构建树形距离结构表示定性属性间的局部顺序关系
- 联合学习机制迭代优化树结构与聚类结果，显著提升精度
- 适合处理症状、婚姻状况等非数值型数据的聚类任务
聚类是理解数据模式的基础方法，通常依赖直观的欧氏距离空间。然而，对于症状、婚姻状况等属性的定性取值所反映的隐含聚类分布，欧氏距离并不适用。本文发现一种树状距离结构，可灵活表示单个属性内定性取值间的局部顺序关系：将每个取值视为树的节点，能捕捉该节点与其他取值之间的丰富顺序关系。为获得适合聚类的树结构，提出联合学习机制，迭代优化更合适的树结构与聚类结果。实验表明，整个数据集的潜在距离空间可通过学习得到的树森林有效表示。在12个真实基准数据集上，与10种对比方法进行显著性检验，验证了所提方法的优越性。
原文摘要 · Abstract (English)
Clustering is a fundamental approach to understanding data patterns, wherein the intuitive Euclidean distance space is commonly adopted. However, this is not the case for implicit cluster distributions reflected by qualitative attribute values, e.g., the nominal values of attributes like symptoms, marital status, etc. This paper, therefore, discovered a tree-like distance structure to flexibly represent the local order relationship among intra-attribute qualitative values. That is, treating a value as the vertex of the tree allows to capture rich order relationships among the vertex value and the others. To obtain the trees in a clustering-friendly form, a joint learning mechanism is proposed to iteratively obtain more appropriate tree structures and clusters. It turns out that the latent distance space of the whole dataset can be well-represented by a forest consisting of the learned trees. Extensive experiments demonstrate that the joint learning adapts the forest to the clustering task to yield accurate results. Comparisons of 10 counterparts on 12 real benchmark datasets with significance tests verify the superiority of the proposed method.
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