Demand-driven frequent itemset mining using pattern structures
Knowledge and Information Systems
Learning quantifiable associations via principal sparse non-negative matrix factorization
Intelligent Data Analysis
Mining quantitative associations in large database
APWeb'05 Proceedings of the 7th Asia-Pacific web conference on Web Technologies Research and Development
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Association rule mining aims at discovering patternswhose support is beyond a given threshold. Mining patternscomposed of items described by an arbitrary subset ofattributes in a large relational table represents a new challengeand has various practical applications, including theevent management systems that motivated this work. Theattribute combinations that define the items in a pattern providethe structural information of the pattern. Current associationalgorithms do not make full use of the structuralinformation of the patterns: the information is either lostafter it is encoded with attribute values, or is constrainedby a given hierarchy or taxonomy. Pattern structures conveyimportant knowledge about the patterns. In this paper,we present a novel architecture that organizes the miningspace based on pattern structures. By exploiting the inter-relationshipsamong pattern structures, execution times formining can be reduced significantly. This advantage isdemonstrated by our experiments using both synthetic andreal-life datasets.