Itemset trees for targeted association querying

Association mining techniques search for groups of frequently co-occurring items in a market-basket type of data and turn these groups into business-oriented rules. Previous research has focused predominantly on how to obtain exhaustive lists of such associations. However, users often prefer a quick...

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Bibliographic Details
Published in:IEEE transactions on knowledge and data engineering 2003-11, Vol.15 (6), p.1522-1534
Main Authors: Kubat, M., Hafez, A., Raghavan, V.V., Lekkala, J.R., Wei Kian Chen
Format: Article
Language:English
Subjects:
Acceleration
Algorithm design and analysis
Baskets
Buying
Counting circuits
Customers
Dairy products
Distributed databases
Fruits
Itemsets
Markets
Queries
Software libraries
Transaction databases
Trees
Web pages
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Summary:Association mining techniques search for groups of frequently co-occurring items in a market-basket type of data and turn these groups into business-oriented rules. Previous research has focused predominantly on how to obtain exhaustive lists of such associations. However, users often prefer a quick response to targeted queries. For instance, they may want to learn about the buying habits of customers that frequently purchase cereals and fruits. To expedite the processing of such queries, we propose an approach that converts the market-basket database into an itemset tree. Experiments indicate that the targeted queries are answered in a time that is roughly linear in the number of market baskets, N. Also, the construction of the itemset tree has O(N) space and time requirements. Some useful theoretical properties are proven.
ISSN:1041-4347
1558-2191
DOI:10.1109/TKDE.2003.1245290