1. Department of Computer Science and Engineering, School of Information Science and Engineering, Yunnan University, Kunming 650091, China 2. Department of Economics, State University of New York at Binghamton, Binghamton NY13902-6001, USA 3. Faculty of Economics and Administration, University of Malaya, Kuala Lumper 50603, Malaysia
Discovering the hierarchical structures of different classes of object behaviors can satisfy the requirements of various degrees of abstraction in association analysis, behavior modeling, data preprocessing, pattern recognition and decision making, etc. In this paper, we call this process as associative categorization, which is different from classical clustering, associative classification and associative clustering. Focusing on representing the associations of behaviors and the corresponding uncertainties, we propose the method for constructing a Markov network (MN) from the results of frequent pattern mining, called item-associative Markov network (IAMN), where nodes and edges represent the frequent patterns and their associations respectively. We further discuss the properties of a probabilistic graphical model to guarantee the IAMN’s correctness theoretically. Then, we adopt the concept of chordal to reflect the closeness of nodes in the IAMN. Adopting the algorithm for constructing join trees from an MN, we give the algorithm for IAMN-based associative categorization by hierarchical bottom-up aggregations of nodes. Experimental results show the effectiveness, efficiency and correctness of our methods.
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