Semi-supervised clustering: probabilistic models, algorithms and experiments
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Clustering is one of the most common data mining tasks, used frequently for data categorization and analysis in both industry and academia. The focus of our research is on semi-supervised clustering, where we study how prior knowledge, gathered either from automated information sources or human supervision, can be incorporated into clustering algorithms. In this thesis, we present probabilistic models for semi-supervised clustering, develop algorithms based on these models and empirically validate their performances by extensive experiments on data sets from different domains, e.g., text analysis, hand-written character recognition, and bioinformatics. In many domains where clustering is applied, some prior knowledge is available either in the form of labeled data (specifying the category to which an instance belongs) or pairwise constraints on some of the instances (specifying whether two instances should be in same or different clusters). In this thesis, we first analyze effective methods of incorporating labeled supervision into prototype-based clustering algorithms, and propose two variants of the well-known KMeans algorithm that can improve their performance with limited labeled data. We then focus on the problem of semi-supervised clustering with constraints and show how this problem can be studied in the framework of a well-defined probabilistic generative model of a Hidden Markov Random Field. We derive an efficient KMeans-type iterative algorithm, HMRF-KMeans, for optimizing a semi-supervised clustering objective function defined on the HMRF model. We also give convergence guarantees of our algorithm for a large class of clustering distortion measures (e.g., squared Euclidean distance, KL divergence, and cosine distance). Finally, we develop an active learning algorithm for acquiring maximally informative pairwise constraints in an interactive query-driven framework, which to our knowledge is the first active learning algorithm for semi-supervised clustering with constraints. Other interesting problems of semi-supervised clustering that we discuss in this thesis include (1) semi-supervised graph-based clustering using kernels, (2) using prior knowledge to improve overlapping clustering of data, (3) integration of both constraintbased and distance-based semi-supervised clustering methods using the HMRF model, and (4) model selection techniques that use the available supervision to automatically select the right number of clusters.