FP-tax: tree structure based generalized association rule mining

Authors:
Iko Pramudiono;Masaru Kitsuregawa
Affiliations:
The University of Tokyo, Tokyo, Japan;The University of Tokyo, Tokyo, Japan
Venue:
Proceedings of the 9th ACM SIGMOD workshop on Research issues in data mining and knowledge discovery
Year:
2004

Citing 7
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Abstract

Data mining has been widely recognized as a powerful tool to explore added value from large-scale databases. One of data mining techniques, generalized association rule mining with taxonomy, is potential to discover more useful knowledge than ordinary flat association rule mining by taking application specific information into account. We propose pattern growth mining paradigm based FP-tax algorithm, which employs a tree structure to compress the database. Two methods to traverse the tree structure are examined: Bottom-Up and Top-Down. Experimental results show that both methods significantly outperform classic Cumulate algorithm, in particular Top-Down FP-tax can achieve two order of magnitudes better performance than Cumulate.