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Efficient Mining of Fuzzy Frequent Itemsets with Type-2 Membership Functions

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Intelligent Information and Database Systems (ACIIDS 2016)

Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 9622))

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Abstract

In the past, the Apriori-based algorithm with fuzzy type-2 membership functions was designed for discovering fuzzy association rules, which is very time-consuming to generate-and-test candidates in a level-wise way. In this paper, we present a list-based fuzzy mining algorithm to mine the fuzzy frequent itemsets with fuzzy type-2 membership functions. A fuzzy-list structure and an efficient pruning strategy are respectively designed to speed up the mining process of fuzzy frequent itemsets. Several experiments are carried to verify the efficiency and effectiveness of the designed algorithm compared to the state-of-the-art Apriori-based algorithm in terms of runtime and number of traversal nodes (candidates).

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Acknowledgements

This research was partially supported by the National Natural Science Foundation of China (NSFC) under grant No.61503092, by the Tencent Project under grant CCF-TencentRAGR20140114, and by the Shenzhen Strategic Emerging Industries Program under grant ZDSY20120613125016389.

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Authors and Affiliations

  1. School of Computer Science and Technology, Harbin Institute of Technology Shenzhen Graduate School, Harbin, China

    Jerry Chun-Wei Lin, Xianbiao Lv & Tsu-Yang Wu

  2. School of Natural Sciences and Humanities, Harbin Institute of Technology Shenzhen Graduate School, Harbin, China

    Philippe Fournier-Viger

  3. Department of Computer Science and Information Engineering, National University of Kaohsiung, Taiwan, China

    Tzung-Pei Hong

  4. Department of Computer Science and Engineering, National Sun Yat-sen University, Taiwan, China

    Tzung-Pei Hong

Authors
  1. Jerry Chun-Wei Lin
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  2. Xianbiao Lv
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  3. Philippe Fournier-Viger
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  4. Tsu-Yang Wu
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  5. Tzung-Pei Hong
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Corresponding author

Correspondence to Jerry Chun-Wei Lin .

Editor information

Editors and Affiliations

  1. Wrocław University of Technology, Wrocław, Poland

    Ngoc Thanh Nguyen

  2. Wrocław University of Technology, Wrocław, Poland

    Bogdan Trawiński

  3. Iwate Prefectural University, Takizawa, Japan

    Hamido Fujita

  4. National University of Kaohsiung, Kaohsiung, Taiwan

    Tzung-Pei Hong

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Lin, J.CW., Lv, X., Fournier-Viger, P., Wu, TY., Hong, TP. (2016). Efficient Mining of Fuzzy Frequent Itemsets with Type-2 Membership Functions. In: Nguyen, N.T., Trawiński, B., Fujita, H., Hong, TP. (eds) Intelligent Information and Database Systems. ACIIDS 2016. Lecture Notes in Computer Science(), vol 9622. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-662-49390-8_18

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  • DOI: https://doi.org/10.1007/978-3-662-49390-8_18

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-662-49389-2

  • Online ISBN: 978-3-662-49390-8

  • eBook Packages: Computer ScienceComputer Science (R0)

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