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: 2015
• 资助国家: 加拿大
• 起止时间: 2015-01-01 至 2016-12-31
• 项目状态: 已结题
• 来源: https://www.nserc-crsng.gc.ca/ase-oro/Details-Detailles_eng.asp?id=570897
• 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. Main objectives of the proposed research program: 1) Advancement of the theoretical understanding of clustering methods applied to today's very high-dimensional data sets, particularly for the following relatively recent approaches "projected (or subspace-) clustering" and "semi-supervised clustering". 2) Development of novel and improved algorithms for projected and semi-supervised clustering, overcoming some of their current limitations, extending their applicability, and also combining the concepts of both for a wider range of application areas where such clustering methods can be useful. 3) Demonstration of the usefulness of the proposed methods on some real world data sets including gene expression data, text data, and medical image plus clinical data.

聚类是一种主要的无监督数据挖掘方法，在应用于当今由自动化设备(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的方差、以及包括大量“不相关的”或“随机的”维度使得通常不可能使用具有普通的全维(非)相似性度量的标准聚类算法来检测有意义的聚类结构。更确切地说，可以通过考虑较低维子空间，或者通过考虑背景知识是否可用(通常作为数据点的一个小子集的“必须链接”或“不能链接”约束)来检测有意义的结构，以将算法引导到与该信息一致的特定集群结构--在一定程度上覆盖在全维空间中导出的信息。 拟议研究计划的主要目标： 1)促进了对适用于当今高维数据集的聚类方法的理论理解，特别是对于以下相对较新的方法：投影(或子空间)聚类和半监督聚类。 2)发展新的和改进的投影和半监督聚类算法，克服它们目前的一些局限性，扩展它们的适用性，并将两者的概念结合起来，用于更广泛的应用领域，在这些领域，这种聚类方法是有用的。 3)在实际数据集(包括基因表达数据、文本数据和医学图像加临床数据)上验证了所提方法的有效性。