Abstract
Mining frequent patterns has been a topic of active research because it is computationally the most expensive step in association rule discovery. In this paper, we discuss the use of compact data structure design for improving the efficiency of frequent pattern mining. It is based on our work in developing efficient algorithms that outperform the best available frequent pattern algorithms on a number of typical data sets. We discuss improvements to the data structure design that has resulted in faster frequent pattern discovery. The performance of our algorithms is studied by comparing their running times on typical test data sets against the fastest Apriori, Eclat, FP-Growth and OpportuneProject algorithms. We discuss the performance results as well as the strengths and limitations of our algorithms.
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Gopalan, R.P., Sucahyo, Y.G. (2003). Improving the Efficiency of Frequent Pattern Mining by Compact Data Structure Design. In: Liu, J., Cheung, Ym., Yin, H. (eds) Intelligent Data Engineering and Automated Learning. IDEAL 2003. Lecture Notes in Computer Science, vol 2690. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-45080-1_79
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DOI: https://doi.org/10.1007/978-3-540-45080-1_79
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-40550-4
Online ISBN: 978-3-540-45080-1
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