Frequent pattern mining searches data for sets of items that are frequently co-occurring together. Most of algorithms find all the frequent patterns. However, there are many real-life situations in which users is interested in only some small portions of the entire collection of frequent patterns. To mine patterns that satisfy the user aggregate constraints in the form of agg(X.attr)θconst, properties of constraints are exploited. When agg is sum, the mining can be complicated. Existing mining systems or algorithms usually make assumptions about the value or range of X.attr and/or const. In this paper, we propose a frequent pattern mining system that avoids making these assumptions and that effectively handles the sum constraints as well as other aggregate constraints.
Numerous frequent itemset mining algorithms have been proposed over the past two decades. Most of them mine traditional databases of precise data. However, there are many real-life applications for which data are uncertain. This leads to the mining of uncertain data. In this paper, we propose an equivalence class transformation based algorithm-called UV-Eclat-which transforms probabilistic databases of uncertain data from their usual horizontal format into a vertical format, from which frequent itemsets are mined.
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