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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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References

  1. Agrawal, R., Imieliski, T., Swami, A.: Mining association rules between sets of items in large databases. ACM SIGMOD Record, pp. 207–216 (1993)

    Google Scholar 

  2. Agrawal, R., Srikant, R.: Quest synthetic data generator (1994). http://www.Almaden.ibm.com/cs/quest/syndata.html

    Google Scholar 

  3. Agrawal, R., Srikant, R.: Fast algorithms for mining association rules in large databases. In: The International Conference on Very Large Databases, pp. 487–499 (1994)

    Google Scholar 

  4. Chen, M.S., Han, J., Yu, P.S.: Data mining: an overview from a database perspective. IEEE Trans. Knowl. Data Eng. 8(6), 866–883 (1996)

    Article  Google Scholar 

  5. Chen, C.-H., Hong, T.-P., Li, Y.: Fuzzy association rule mining with type-2 membership functions. In: Nguyen, N.T., Trawiński, B., Kosala, R. (eds.) ACIIDS 2015. LNCS, vol. 9012, pp. 128–134. Springer, Heidelberg (2015)

    Google Scholar 

  6. Han, J., Pei, J., Yin, Y., Mao, R.: Mining frequent patterns without candidate generation: a frequent-pattern tree approach. Data Min. Knowl. Discov. 8, 53–87 (2004)

    Article  MathSciNet  Google Scholar 

  7. Hong, T.P., Kuo, C.S., Chi, S.C.: Mining association rules from quantitative data. Intell. Data Anal. 3, 363–376 (1999)

    Article  MATH  Google Scholar 

  8. Karnik, N.N., Mendel, J.M.: Introduction to type-2 fuzzy logic systems. In: International Conference on Fuzzy Systems, pp. 915–920 (1998)

    Google Scholar 

  9. Kuok, C.M., Fu, A., Wong, M.H.: Mining fuzzy association rules in databases. ACM SIGMOD Record 27, 41–46 (1998)

    Article  Google Scholar 

  10. Lin, C.W., Hong, T.P., Lu, W.H.: Linguistic data mining with fuzzy FP-trees. Expert Syst. Appl. 37, 4560–4567 (2010)

    Article  Google Scholar 

  11. Lin, J.C.W., Hong, T.P., Lin, T.C.: A CMFFP-tree algorithm to mine complete multiple fuzzy frequent itemsets. Appl. Soft Comput. 28, 431–439 (2015)

    Article  Google Scholar 

  12. Lin, C.W., Hong, T.P.: Mining fuzzy frequent itemsets based on UBFFP trees. J. Intell. Fuzzy Syst. 27, 535–548 (2014)

    MathSciNet  Google Scholar 

  13. Mendel, J.M., John, R.I.B.: Type-2 fuzzy sets made simple. IEEE Trans. Fuzzy Syst. 10, 117–127 (2002)

    Article  Google Scholar 

  14. Melin, P., Castillo, O.: A review on the applications of type-2 fuzzy logic in classification and pattern recognition. Expert Syst. Appl. 40, 5413–5423 (2013)

    Article  Google Scholar 

  15. SPMF: an open-source data mining library. http://www.philippe-fournier-viger.com/spmf/index.php

  16. Zadeh, L.A.: Fuzzy sets. Inf. Control 8, 338–353 (1965)

    Article  MathSciNet  MATH  Google Scholar 

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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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