The problem of selecting a small, yet high quality subset of patterns from a larger collection of itemsets has recently attracted a lot of research. Here we discuss an approach to this problem using the notion of decomposable families of itemsets. Such itemset families define a probabilistic model for the data from which the original collection of itemsets was derived. Furthermore, they induce a special tree structure, called a junction tree, familiar from the theory of Markov Random Fields. The method has several advantages. The junction trees provide an intuitive representation of the mining results. From the computational point of view, the model provides leverage for problems that could be intractable using the entire collection of itemsets. We provide an efficient algorithm to build decomposable itemset families, and give an application example with frequency bound querying using the model. An empirical study show that our algorithm yields high quality results. © 2008 Springer-Verlag Berlin Heidelberg.
CITATION STYLE
Tatti, N., & Heikinheimo, H. (2008). Decomposable families of itemsets. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (Vol. 5212 LNAI, pp. 472–487). https://doi.org/10.1007/978-3-540-87481-2_31
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