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International Conference on Industrial, Engineering and Other Applications of Applied Intelligent Systems

IEA/AIE 2019: Advances and Trends in Artificial Intelligence. From Theory to Practice pp 216–229Cite as

An Efficient Algorithm for Deriving Frequent Itemsets from Lossless Condensed Representation

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Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 11606))

Abstract

Mining frequent itemsets (abbr. FIs) from dense databases usually generates a large amount of itemsets, causing the mining algorithms to suffer from long execution time and high memory usage. Frequent closed itemset (abbr. FCI) is a lossless condensed representation of FI. Mining only the FCIs allows to reducing the execution time and memory usage. Moreover, with correct methods, the complete information of FIs can be derived from FCIs. Although many studies have presented various efficient approaches for mining FCIs, few of them have developed efficient algorithms for deriving FIs from FCIs. In view of this, we propose a novel algorithm called DFI-Growth for efficiently deriving FIs from FCIs. Moreover, we propose two strategies, named maximum support selection and maximum support replacement to guarantee that all the FIs and their supports can be correctly derived by DFI-Growth. To the best of our knowledge, the proposed DFI-Growth is the first kind of tree-based and pattern growth algorithm for deriving FIs from FCIs. Experiments show that DFI-Growth is superior to the most advanced deriving algorithm [12] in terms of both execution time and memory consumption.

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Acknowledgment

This work is supported in part by Ministry of Science and Technology, Taiwan, ROC under grant no. 107-2218-E-197-002 and 108-2634-F-009-010.

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Authors and Affiliations

  1. Department of Computer Science and Information Engineering, National Ilan University, Yilan City, Taiwan, ROC

    JianTao Huang, Yi-Pei Lai, Chieh Lo & Cheng-Wei Wu

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  1. JianTao Huang
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  2. Yi-Pei Lai
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  3. Chieh Lo
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  4. Cheng-Wei Wu
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Corresponding author

Correspondence to Cheng-Wei Wu .

Editor information

Editors and Affiliations

  1. Institute for Software Technology, Graz University of Technology, Graz, Austria

    Franz Wotawa

  2. Department of Applied Informatics, University of Klagenfurt, Klagenfurt, Austria

    Gerhard Friedrich

  3. Institute for Software Technology, Graz University of Technology, Graz, Austria

    Ingo Pill

  4. Institute for Software Technology, Graz University of Technology, Graz, Austria

    Roxane Koitz-Hristov

  5. Department of Computer Science, Texas State University, San Marcos, TX, USA

    Moonis Ali

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Huang, J., Lai, YP., Lo, C., Wu, CW. (2019). An Efficient Algorithm for Deriving Frequent Itemsets from Lossless Condensed Representation. In: Wotawa, F., Friedrich, G., Pill, I., Koitz-Hristov, R., Ali, M. (eds) Advances and Trends in Artificial Intelligence. From Theory to Practice. IEA/AIE 2019. Lecture Notes in Computer Science(), vol 11606. Springer, Cham. https://doi.org/10.1007/978-3-030-22999-3_20

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  • DOI: https://doi.org/10.1007/978-3-030-22999-3_20

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-22998-6

  • Online ISBN: 978-3-030-22999-3

  • eBook Packages: Computer ScienceComputer Science (R0)

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