Provided by: WSDOT
Topic: Data Management
Existing algorithms for utility mining are column enumeration based, adopt an Apriori-like candidate set generation-and-test approach, and thus are inadequate on datasets with high dimensions or long patterns. To solve the problem, this paper proposes a hybrid model and a row enumeration based algorithm, i.e., inter-transaction, to discover high utility itemsets from two directions: existing algorithms such as UMining can be used to seek short high utility itemsets from the bottom, while inter-transaction seeks long high utility itemsets from the top. By intersecting relevant transactions, the new algorithm can identify long high utility itemsets directly, without extending short itemsets step by step.