Mining Generalized Multiple-Level Association Rules

Authors:
Show-Jane Yen
Affiliations:
-
Venue:
PKDD '00 Proceedings of the 4th European Conference on Principles of Data Mining and Knowledge Discovery
Year:
2000

Citing 4
Cited 2

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Mining Generalized Association Rules

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Preknowledge-based generalized association rules mining

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Generalized association rule mining using an efficient data structure

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Abstract

Mining association rules is an important task for knowledge discovery. We can analyze past transaction data to discover customer behaviors such that the quality of business decision can be improved. The strategy of mining association rules focuses on discovering large itemsets which are groups of items which appear together in a sufficient number of transactions. In this paper, we propose a graph-based approach to generate generalized multiple-level association rules from a large database of customer transactions, which describes the associations among items in any concept level. This approach is to scan the database once to construct an association graph, and then traverse the graph to generate large itemsets.