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EFP-M2 : efficient model for mining frequent patterns in transactional database
conference contribution
posted on 2012-01-01, 00:00 authored by T Herawan, A Noraziah, Z Abdullah, M Deris, Jemal AbawajyJemal AbawajyDiscovering frequent patterns plays an essential role in many data mining applications. The aim of frequent patterns is to obtain the information about the most common patterns that appeared together. However, designing an efficient model to mine these patterns is still demanding due to the capacity of current database size. Therefore, we propose an Efficient Frequent Pattern Mining Model (EFP-M2) to mine the frequent patterns in timely manner. The result shows that the algorithm in EFP-M2l is outperformed at least at 2 orders of magnitudes against the benchmarked FP-Growth.
History
Event
Computational Collective Intelligence. Conference (4th : 2012 : Ho Chi Minh City, Vietnam)Source
Computational collective intelligence : technologies and applications : 4th international conference, ICCCI 2012, Ho Chi Minh City, Vietnam, November 28-30, 2012 : proceedingsSeries
Lecture notes in artificial intelligence; Lecture notes in computer science, v7654Pagination
29 - 38Publisher
Springer Berlin HeidelbergLocation
Ho Chi Minh City, VietnamPlace of publication
Berlin, GermanyStart date
2012-11-28End date
2012-11-30ISSN
0302-9743ISBN-13
9783642347061Language
engPublication classification
E1 Full written paper - refereedCopyright notice
2012, SpringerExtent
56Editor/Contributor(s)
N Nguyen, K Hoang, P JedrzejowiczTitle of proceedings
ICCCI 2012 : Technologies and applications : Proceedings of the 4th International Conference on Computational Collective Intelligence, Ho Chi Minh City, Vietnam, November 28-30, 2012 : proceedingsUsage metrics
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