Title

Mining And Validating Localized Frequent Itemsets With Dynamic Tolerance

Keywords

Association rules; Error tolerant itemsets; Frequent itemsets; Frequent patterns; Market basket analysis

Abstract

We cast the frequent itemset mining problem as a criterion guided optimization problem instead of one based on exact counting. This opens several interesting possibilities, including modification of the criterion function to take into account (i) error tolerance, (ii) locality, (iii) unsupervised estimation of the error tolerance, and (iv) search strategy. We also propose a new validation procedure that takes into account the completeness and accuracy of the discovered patterns. Experiments with real Web transaction data are presented.

Publication Date

1-1-2006

Publication Title

Proceedings of the Sixth SIAM International Conference on Data Mining

Volume

2006

Number of Pages

579-583

Document Type

Article; Proceedings Paper

Personal Identifier

scopus

DOI Link

https://doi.org/10.1137/1.9781611972764.64

Socpus ID

33745446613 (Scopus)

Source API URL

https://api.elsevier.com/content/abstract/scopus_id/33745446613

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