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: 2014
• 资助国家: 加拿大
• 起止时间: 2014-01-01 至 2015-12-31
• 项目状态: 已结题
• 来源: https://www.nserc-crsng.gc.ca/ase-oro/Details-Detailles_eng.asp?id=547601
• 关键词: Projected; semi; supervised; clustering; dimensional; 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.

聚类是应用于当今高维数据集时面临严重挑战的主要无监督数据挖掘方法之一，这些方法是通过自动设备（例如微阵列芯片，传感器，记录设备）大规模收集的。数据的稀疏性，全维空间中距离的较小差异以及包含大量“无关”或“随机”维度的稀疏性，通常不可能使用标准的聚类算法使用具有共同的全维（DIS-）相似性度量来检测有意义的聚类结构。有意义的结构可以通过考虑较低的子空间来检测到有意义的结构，或者考虑到背景知识是否可用（通常为“必链接”或“无法链接”的数据点的限制），以指导算法，以指导某些与此信息一致的聚类结构 - 与某种程度的信息相一致，以使其在某种程度上散发出完整的信息。