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

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.

聚类是主要的无监督数据挖掘方法之一，在应用于当今由自动化设备（例如微阵列芯片、传感器、记录设备）大规模收集的高维数据集时面临着严峻的挑战。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”维度使得通常不可能使用常见的标准聚类算法来检测有意义的聚类结构。全维度（不）相似性度量。有意义的结构可以通过考虑较低维子空间来检测，或者通过考虑背景知识（如果可用）（通常作为数据点的小子集的“必须链接”或“不能链接”约束）来检测将算法引导至与该信息一致的某种聚类结构——在某种程度上覆盖全维空间中导出的信息。