Efficient search for association rules

By Geoffrey I. Webb. 2000

215 citations recorded by the source corpus.

This paper argues that for some applications direct search for association rules can be more e cient than the tw o stage process of the Apriori algorithm which rst nds large itemsets whic hare then used to iden tify associations. In particular, it is argued, Apriori can impose large computational overheads when the number of frequen titemsets is very large. This will often be the case when association rule analysis is performed on domains other than basket analysis or when it is performed for basket analysis with basket information augmented b y other customer information. An algorithm is presented that is computationally e cient for association rule analyses during which the n umber of rules to be found can be constrained and all data can be maintained in memory.

Subfields: Computational Theory and Mathematics, Information Systems, Signal Processing

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Citations in the corpus

Black-Box vs. White-Box: Understanding Their Advantages and Weaknesses From a Practical Point of View (2019, neutral): “cannot be handled by experts effectively [132].”
Rules for contrast sets (2010, unknown): “In fact, our implementation is towards proposals like OPUS search [15] and [20] or even a more recent approach to discrimination rules like [12].”
Foundations of Rule Learning (2012, methodology): “Follow-up work (Webb, 2000; Webb & Zhang, 2005) has shown that this technique is also an efficient alternative for association rule discovery, provided that the database to mine fits into the memory of the learning system.”
Data Mining Techniques (2001, methodology): “This approach was also applied to association rule mining in Webb (2000).”
Efficient Adaptive-Support Association Rule Mining for Recommender Systems (2004, background): “Recent work (Webb, 2000; Agarwal et al., 2000) has begun to address some of these concerns by means of judicious search techniques.”
Collaborative Recommendation via Adaptive Association Rule Mining (2000, methodology): “A di erent rule mining algorithm that produces rulesets of constrained size is presented in [18].”
Survey on Frequent Pattern Mining (2003, methodology): “Nevertheless, there exist a successful algorithm, called MagnumOpus, that uses another strategy to immediately generate a large subset of all association rules [ 40 ].”
Explainable and Interpretable Models in Computer Vision and Machine Learning (2017, background): “Only few algorithms directly find rules that optimize a given score function (Webb 2000).”