Projected and semi-supervised clustering for high-dimensional data

聚类是主要的无监督数据挖掘方法之一，当应用于今天的高维数据集时面临着严峻的挑战，这些数据集是由自动设备（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具有常见全维（不）相似性度量的标准聚类算法来检测有意义的聚类结构。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（如果它是可用的）（通常作为数据点的小子集的“必须链接”或“不能链接”约束）来指导算法到与此信息一致的某个聚类结构-在某种程度上覆盖全维空间中导出的信息。

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.