Proceedings of the Korea Information Processing Society Conference (한국정보처리학회:학술대회논문집)
- 2007.05a
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- Pages.31-33
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- 2007
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- 2005-0011(pISSN)
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- 2671-7298(eISSN)
I-Tree: A Frequent Patterns Mining Approach without Candidate Generation or Support Constraint
- Tanbeer, Syed Khairuzzaman (Dept. of Computer Engineering, Kyung Hee University) ;
- Sarkar, Jehad (Dept. of Computer Engineering, Kyung Hee University) ;
- Jeong, Byeong-Soo (Dept. of Computer Engineering, Kyung Hee University) ;
- Lee, Young-Koo (Dept. of Computer Engineering, Kyung Hee University) ;
- Lee, Sung-Young (Dept. of Computer Engineering, Kyung Hee University)
- Published : 2007.05.11
Abstract
Devising an efficient one-pass frequent pattern mining algorithm has been an issue in data mining research in recent past. Pattern growth algorithms like FP-Growth which are found more efficient than candidate generation and test algorithms still require two database scans. Moreover, FP-growth approach requires rebuilding the base-tree while mining with different support counts. In this paper we propose an item-based tree, called I-Tree that not only efficiently mines frequent patterns with single database scan but also provides multiple mining scopes with multiple support thresholds. The 'build-once-mine-many' property of I-Tree allows it to construct the tree only once and perform mining operation several times with the variation of support count values.
Keywords