The Graduate School SEMI - SUPERVISED CLUSTERING FOR HIGH - DIMENSIONAL AND SPARSE FEATURES

Clustering is one of the most common data mining tasks, used frequently for data organization and analysis in various application domains. Traditional machine learning approaches to clustering are fully automated and unsupervised where class labels are unknown a priori. In real application domains, however, some “weak” form of side information about the domain or data sets can be often available or derivable. In particular, information in the form of instance-level pairwise constraints is general and is relatively easy to derive. The problem with traditional clustering techniques is that they cannot benefit from side information even when available. I study the problem of semi-supervised clustering, which aims to partition a set of unlabeled data items into coherent groups given a collection of constraints. Because semi-supervised clustering promises higher quality with little extra human effort, it is of great interest both in theory and in practice. Semi-supervised clustering shares a difficulty with a large number of other learning methods in data mining literature. That is, they lose their algorithmic effectiveness for high dimensional data. I focus on data with high-dimensional sparse features and present a series of novel semi-supervised clustering approaches that are