Decomposable families of itemsets

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Journal Title
Journal ISSN
Volume Title
Faculty of Information and Natural Sciences | D4 Julkaistu kehittämis- tai tutkimusraportti taikka -selvitys
Date
2008
Major/Subject
Mcode
Degree programme
Language
en
Pages
iv, 16
Series
TKK reports in information and computer science, 1
Abstract
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 themining 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.
Description
Keywords
itemsets, decomposable models, Boolean query
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Permanent link to this item
https://urn.fi/urn:nbn:fi:tkk-011456