Abstract
One of the goals of Association Mining is to develop algorithms capable of finding frequently co-occurring groups of items (itemsets) in transaction databases. The recently published technique of Itemset Trees expedited the processing of so-called targeted queries where the user is interested only in itemsets that contain certain prespecified items. However, the technique did not seem to offer any cost-effective way how to find all frequent itemsets (general queries) as it is common with other association-mining algorithms. The purpose of this paper is to rectify this deficiency by a newly developed algorithm that we call IT-Mining. Experimental results indicate that itemset trees can now with advantage be used to answer both targeted and general queries, and that the technique compares favorably with previous atttempts under a broad range of data parameters.
Original language | English (US) |
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Pages (from-to) | 105-120 |
Number of pages | 16 |
Journal | Intelligent Data Analysis |
Volume | 10 |
Issue number | 2 |
DOIs | |
State | Published - Jan 1 2006 |
Keywords
- Data mining
- frequent itemsets
- itemset trees
ASJC Scopus subject areas
- Artificial Intelligence
- Theoretical Computer Science
- Computer Vision and Pattern Recognition