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A Model-Based Frequency Constraint for Mining Associations from Transaction Data

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Abstract

Mining frequent itemsets is a popular method for finding associated items in databases. For this method, support, the co-occurrence frequency of the items which form an association, is used as the primary indicator of the associations's significance. A single, user-specified support threshold is used to decided if associations should be further investigated. Support has some known problems with rare items, favors shorter itemsets and sometimes produces misleading associations.

In this paper we develop a novel model-based frequency constraint as an alternative to a single, user-specified minimum support. The constraint utilizes knowledge of the process generating transaction data by applying a simple stochastic mixture model (the NB model) which allows for transaction data's typically highly skewed item frequency distribution. A user-specified precision threshold is used together with the model to find local frequency thresholds for groups of itemsets. Based on the constraint we develop the notion of NB-frequent itemsets and adapt a mining algorithm to find all NB-frequent itemsets in a database. In experiments with publicly available transaction databases we show that the new constraint provides improvements over a single minimum support threshold and that the precision threshold is more robust and easier to set and interpret by the user.

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Notes

  1. Although the artificial data set in this paper and in Zheng et al. (2001) were produced using the same generator (available at http://www.almaden.ibm.com/software/quest/Resources/), there are minimal variations due to differences in the used random number generator initialization.

  2. We used a machine with two Intel Xeon processors (2.4 GHz) running Linux (Debian Sarge). The algorithm was implemented in JAVA and compiled using the gnu ahead-of-time compiler gcj version 3.3.5. CPU time was recorded using the time command and we report the sum of user and system time.

  3. Available at http://fuzzy.cs.uni-magdeburg.de/~borgelt/software.html

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  1. Vienna University of Economics and Business Administration, Vienna, Austria

    Michael Hahsler

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  1. Michael Hahsler
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Correspondence to Michael Hahsler.

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Hahsler, M. A Model-Based Frequency Constraint for Mining Associations from Transaction Data. Data Min Knowl Disc 13, 137–166 (2006). https://doi.org/10.1007/s10618-005-0026-2

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