Abstract
Conventional frequent pattern mining algorithms require users to specify some minimum support threshold, which is not easy to identify without knowledge about the datasets in advance. This difficulty leads users to dilemma that either they may lose useful information or may not be able to screen for the interesting knowledge from huge presented frequent patterns sets. Mining top-k frequent patterns allows users to control the number of patterns to be discovered for analyzing. In this paper, we propose an optimized version of the ExMiner, called OExMiner, to mine the top-k frequent patterns from a large scale dataset efficiently and effectively. In order to improve the user-friendliness and also the performance of the system we proposed other 2 methods, extended from OExMiner, called Seq-Miner and Seq-BOMA to mine top-k frequent patterns sequentially. Experiments on both synthetic and real data show that our proposed methods are much more efficient and effective compared to the existing ones.
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Minh, Q.T., Oyanagi, S., Yamazaki, K. (2006). Mining the K-Most Interesting Frequent Patterns Sequentially. In: Corchado, E., Yin, H., Botti, V., Fyfe, C. (eds) Intelligent Data Engineering and Automated Learning – IDEAL 2006. IDEAL 2006. Lecture Notes in Computer Science, vol 4224. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11875581_75
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DOI: https://doi.org/10.1007/11875581_75
Publisher Name: Springer, Berlin, Heidelberg
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