A model-based frequency constraint for mining associations from transaction data

Michael Hahsler

Publication: Working/Discussion PaperWU Working Paper

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In this paper we develop an alternative to minimum support which utilizes knowledge of the process which generates transaction data and allows for highly skewed frequency distributions. We apply a simple stochastic model (the NB model), which is known for its usefulness to describe item occurrences in transaction data, to develop a frequency constraint. This model-based frequency constraint is used together with a precision threshold to find individual support thresholds for groups of associations. We develop the notion of NB-frequent itemsets and present two mining algorithms which find all NB-frequent itemsets in a database. In experiments with publicly available transaction databases we show that the new constraint can provide significant improvements over a single minimum support threshold and that the precision threshold is easier to use. (author's abstract)
Original languageEnglish
Place of PublicationVienna
PublisherInstitut für Informationsverarbeitung und Informationswirtschaft, WU Vienna University of Economics and Business
Publication statusPublished - 2004

Publication series

SeriesWorking Papers on Information Systems, Information Business and Operations

WU Working Paper Series

  • Working Papers on Information Systems, Information Business and Operations

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