A comparison of atttribute selection strategies for attribute-oriented generalization

Barber, Brock; Hamilton, Howard J.

doi:10.1007/3-540-63614-5_10

Cited by 2 publications

(1 citation statement)

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“…The characteristic attribute to generalize first can be determined in accordance with the guidelines specified in [6], where lookahead and predictive strategies are suggested. Using the lookahead strategy, a relation with m attributes is used to generate m new generalized relations, each of which is created by generalizing a different attribute to the next highest level in its associated CH.…”

Section: Discovering Association Rules: An Extended Examplementioning

confidence: 99%

Mining Association Rules From Market Basket Data Using Share Measures and Characterized Itemsets

Hilderman

Hamilton

Carter³

et al. 1998

Int. J. Artif. Intell. Tools

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We propose the share-confidence framework for knowledge discovery from databases which addresses the problem of mining characterized association rules from market basket data {i.e., itemsets). Our goal is to not only discover the buying patterns of customers, but also to discover customer profiles by partitioning customers into distinct classes. We present a new algorithm for classifying itemsets based upon characteristic attributes extracted from census or lifestyle data. Our algorithm combines the Apriori algorithm for discovering association rules between items in large databases, and the AOG algorithm for attribute-oriented generalization in large databases. We show how characterized itemsets can be generalized according to concept hierarchies associated with the characteristic attributes. Finally, we present experimental results that demonstrate the utility of the share-confidence framework.

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Section: Discovering Association Rules: An Extended Examplementioning

confidence: 99%