Projected and semi-supervised clustering for high-dimensional data

高维数据的投影和半监督聚类

• 批准号: 250344-2011
• 负责人: Sander, Jörg
• 金额: $ 2.11万
• 依托单位: University of Alberta
• 依托单位国家: 加拿大
• 项目类别: Discovery Grants Program - Individual
• 财政年份: 2012
• 资助国家: 加拿大
• 起止时间: 2012-01-01 至 2013-12-31
• 项目状态: 已结题
• 来源: https://www.nserc-crsng.gc.ca/ase-oro/Details-Detailles_eng.asp?id=508281
• 关键词: Projected; semi; supervised; clustering; dimensional

Clustering is one of the major unsupervised data mining methods facing severe challenges when applied to today's high-dimensional data sets, which are collected on a large scale by automatic equipment (e.g. microarray chips, sensors, logging devices). The sparsity of the data, the small variance of distances in the full-dimensional space, and the inclusion of a large number of "irrelevant" or "random" dimensions make it typically impossible to detect a meaningful clustering structure using standard clustering algorithms with common full-dimensional (dis-)similarity measures. Meaningful structure can rather be detected by either considering lower-dimensional subspaces, or, by taking into account background knowledge if it is available (often as "must-link" or "cannot-link" constraints for a small subset of data points) to guide an algorithm to a certain clustering structure that is consistent with this information - overriding to some extent the information derived in the full-dimensional space.

聚类是目前主要的无监督数据挖掘方法之一，在应用于自动化设备（如微阵列芯片、传感器、测井设备）大规模收集的高维数据集时面临严峻挑战。数据的稀疏性、全维空间中距离的小方差以及包含大量“不相关”或“随机”维度，使得使用具有常见全维（非）相似性度量的标准聚类算法通常不可能检测到有意义的聚类结构。有意义的结构可以通过考虑低维子空间来检测，或者通过考虑背景知识（通常作为一小部分数据点的“必须链接”或“不能链接”约束）来指导算法获得与该信息一致的特定聚类结构——在一定程度上覆盖在全维空间中派生的信息。