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Published in:
2012 | OriginalPaper | Chapter
EFP-M2: Efficient Model for Mining Frequent Patterns in Transactional Database
Authors : Tutut Herawan, A. Noraziah, Zailani Abdullah, Mustafa Mat Deris, Jemal H. Abawajy
Published in: Computational Collective Intelligence. Technologies and Applications
Publisher: Springer Berlin Heidelberg
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Discovering 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.