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Efficiently Updating the Discovered Multiple Fuzzy Frequent Itemsets with Transaction Insertion

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Abstract

Most pattern mining approaches such as association rule mining or frequent itemsets mining can only handle, however, the binary database in which each item or attribute is represented as 0 or 1. In real-life situations, data may be represented as more complicatedly. Moreover, it is more meaningful for human begins to understand if the discovered rules can be represented as the linguistic terms. Fuzzy-set theory was adopted to handle the quantitative database, which transforms the quantity value into the linguistic terms with their fuzzy values based on the pre-defined membership functions. Several algorithms were designed to handle this issue, but most of them focus on mining the fuzzy rules in the static database. When the database size is changed, the whole updated database should be rescanned again to obtain the up-to-date information. This progress ignores the already discovered knowledge with its computational cost, which is inefficient in dynamic applications. In this paper, we present an incremental multiple fuzzy frequent itemset mining with transaction INSertion (IMF-INS) algorithm to efficiently update the multiple fuzzy frequent itemsets from the quantitative dataset. The designed IMF-INS utilizes the fuzzy FUP concept to divide the transformed linguistic terms into four cases. Each case is performed by the designed approach to update the discovered information. Also, the fuzzy list structure is utilized to reduce the generation of candidates without multiple database scans. Experiments are conducted to show the performance of the proposed algorithm in terms of runtime, memory consumption, number of determined patterns and scalability. Results indicated that the designed IMF-INS outperforms the state-of-the-art batch mining progresses.

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References

  1. Agrawal, R., Imielinski, T., Swami, A.: Database mining: a performance perspective. IEEE Trans. Knowl. Data Eng. 5(6), 914–925 (1993)

    Article  Google Scholar 

  2. Agrawal, R., Srikant, R.: Fast algorithms for mining association rules in large databases. In: International Conference on Very Large Data Base, pp. 619–624, Sept 1994

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

  4. Agrawal, R., Srikant, R.: Mining sequential patterns. In: International Conference on Data Engineering, pp. 3–14, March 1995

  5. Antonelli, M., Ducange, P., Marcelloni, F.: A novel associative classification model based on a fuzzy frequent pattern mining algorithm. Expert Syst. Appl. 42(4), 2086–2097 (2015)

    Article  Google Scholar 

  6. Bansal, M., Grover, D., Sharma, D.: Sensitivity association rule mining using weight based fuzzy logic. Global J. Enterprise Inf. Syst. 9(2), 1–9 (2017)

    Article  Google Scholar 

  7. Cheung, D., Han, J., Ng, V., Wong, C.: Maintenance of discovered association rules in large databases: an incremental updating approach. In: IEEE International Conference on Data Engineering, pp. 106–114, Feb 1996

  8. 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 

  9. Chan, K.C.C., Au, W.H.: Mining fuzzy association rules. In: International Conference on Information and Knowledge Management, pp. 209–215, Jan 1997

  10. Chang, K., Kim, L.J.S., Chung, N.H., Kwon, S.J.: Fuzzy cognitive map approach to web-mining inference amplification. Expert Syst. Appl. 22(3), 197–211 (2002)

    Article  Google Scholar 

  11. Chen, C.H., Hong, T.P., Tseng, V.S.: Fuzzy data mining for time-series data. Appl. Soft Comput. 12(1), 536–542 (2012)

    Article  Google Scholar 

  12. Florez, G., Bridges, S.A., Vaughn, R.B.: An improved algorithm for fuzzy data mining for intrusion detection. Annual Meeting of the North American Fuzzy Information Processing Society, pp. 457–462, June 2002

  13. Fournier-Viger, P., Lin, J.C.W., Gomariz, A., Gueniche, T., Soltani, A., Deng, Z., Lam, H.T.: The SPMF open-source data mining library version 2. In: European Conference on Principles of Data Mining and Knowledge Discovery, pp. 36–40, Sept 2016

    Chapter  Google Scholar 

  14. Fournier-Viger, P., Lin, J.C.W., Vo, B., Chi, T.T., Zhang, J., Le, H.B.: A survey of itemset mining. Wiley Interdiscip. Rev. Data Min. Knowl. Discov. 7(4), 1–18 (2017)

    Article  Google Scholar 

  15. Fournier-Viger, P., Lin, J.C.W., Kiran, R.U., Koh, Y.S., Thomas, R.: A survey of sequential pattern mining. Data Sci. Pattern Recogn. 1(1), 54–77 (2017)

    Google Scholar 

  16. Gan, W., Lin, J.C.W., Chao, H.C., Zhang, J.: Data mining in distributed environment: a survey. WIREs Data Min. Knowl. Discov. 7(6), 1–19 (2017)

    Google Scholar 

  17. Gan, W., Lin, J.C.W., Fournier-Viger, P., Chao, H.C., Fujita, H.: A survey of incremental high-utility itemset mining. WIREs Data Min. Knowl. Discov. 8(2), 1–23 (2018)

    Google Scholar 

  18. Han, J., Pei, J., Yin, Y., Mao, R.: Mining frequent patterns without candidate generation: a frequent-pattern tree approach. Data Min. Knowl. Disc. 8(1), 53–87 (2004)

    Article  MathSciNet  Google Scholar 

  19. Hong, T.P., Kuo, C.S., Chi, S.C.: Mining association rules from quantitative data. Intell. Data Anal. 3(5), 363–376 (1999)

    Article  Google Scholar 

  20. Hong, T.P., Lin, C.W., Wu, Y.L.: Incrementally fast updated frequent pattern trees. Expert Syst. Appl. 34(4), 2424–2435 (2008)

    Article  Google Scholar 

  21. Hong, T.P., Lan, G.C., Lin, Y.H., Pan, S.T.: An effective gradual data-reduction strategy for fuzzy itemset mining. Int. J. Fuzzy Syst. 15(2), 170–181 (2013)

    Google Scholar 

  22. Hong, T.P., Lin, C.W., Lin, T.C.: The MFFP-tree fuzzy mining algorithm to discover complete linguistic frequent itemsets. Comput. Intell. 30(1), 145–166 (2014)

    Article  MathSciNet  Google Scholar 

  23. Kuok, C.M., Fu, A., Wong, M.H.: Mining fuzzy association rules in databases. ACM SIGMOD Record 27(1), 41–46 (1998)

    Article  Google Scholar 

  24. Krishnapuram, R., Joshi, A., Nasraoui, O., Yi, L.: Low-complexity fuzzy relational clustering algorithms for Web mining. IEEE Trans. Fuzzy Syst. 9(4), 595–607 (2001)

    Article  Google Scholar 

  25. Krishnamoorthy, S., Sudha Sadasivam, G., Rajalakshmi, M., Kowsalyaa, K., Dhivya, M.: Privacy preserving fuzzy association rule Mining in data clusters using particle swarm optimization. Int. J. Intell. Inf. Technol. 13(2), 1–20 (2017)

    Article  Google Scholar 

  26. Lin, C.W., Hong, T.P., Lu, W.H.: An effective tree structure for mining high utility itemsets. Expert Syst. Appl. 38(6), 7419–7424 (2011)

    Article  Google Scholar 

  27. Lin, C.W., Lan, G.C., Hong, T.P.: An incremental mining algorithm for high utility itemsets. Expert Syst. Appl. 39(8), 7173–7180 (2012)

    Article  Google Scholar 

  28. Lin, C.W., Hong, T.P.: A survey of fuzzy web mining. Wiley Interdiscip. Rev. Data Min. Knowl. Discov. 3(3), 190–199 (2013)

    Article  Google Scholar 

  29. Lin, C.W., Hong, T.P., Lu, W.H.: Mining fuzzy frequent itemsets based on UBFFP trees. J. Intell. Fuzzy Syst. 27(1), 535–548 (2014)

    MathSciNet  Google Scholar 

  30. Lin, C.W., Hong, T.P., Lin, W.Y., Lan, G.C.: Efficient updating of sequential patterns with transaction insertion. Intell. Data Anal. 18, 1013–1026 (2014)

    Google Scholar 

  31. Lin, J.C.W., Hong, T.P., Lin, T.C., Pan, S.T.: An UBMFFP tree for mining multiple fuzzy frequent itemsets. Int. J. Uncertain. Fuzziness Knowl. Based Syst. 23(6), 861–879 (2015)

    Article  Google Scholar 

  32. Lin, J.C.W., Li, T., Fournier-Viger, P., Hong, T.P.: A fast algorithm for mining fuzzy frequent itemsets. J. Intell. Fuzzy Syst. 29(6), 2373–2379 (2015)

    Article  Google Scholar 

  33. Matthews, S.G., Gongora, M.A., Hopgood, A.A., Ahmadi, S.: Web usage mining with evolutionary extraction of temporal fuzzy association rules. Knowl. Based Syst. 54, 66–72 (2013)

    Article  Google Scholar 

  34. Nasraoui, O., Petenes, C.: Combining web usage mining and fuzzy inference for website personalization. In: International Conference on Web Mining and Web Usage Analysis, pp. 37–46, Aug 2003

  35. Pierrard, R., Poli, J.P., Hudelot, C.: A fuzzy close algorithm for mining fuzzy association rules \(<\)hal-01698352v1\(>\), May 2018

  36. Shitong, W., Chung, K.F.L., Hongbin, S.: Fuzzy taxonomy, quantitative database and mining generalized association rules. Intell. Data Anal. 9(2), 207–217 (2005)

    Google Scholar 

  37. Subramanyam, R.B.V., Goswami, A.: A fuzzy data mining algorithm for incremental mining of quantitative sequential patterns. Int. J. Uncertain. Fuzziness Knowl. Based Syst. 13(6), 633–652 (2005)

    Article  Google Scholar 

  38. Shi, W., Zhang, A., Webb, G.U.: Mining significant crisp-fuzzy spatial association rules. Int. J. Geogr. Inf. Sci. 32(6), 1247–1270 (2018)

    Article  Google Scholar 

  39. Ting, C.K., Liaw, R.T., Wang, T.C., Hong, T.P.: Mining fuzzy association rules using a memetic algorithm based on structure representation. Memet. Comput. 10(1), 15–28 (2018)

    Article  Google Scholar 

  40. Yao, H., Hamilton, H.J.: Mining itemset utilities from transaction databases. Data Knowl. Eng. 59(3), 603–626 (2006)

    Article  Google Scholar 

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

    Article  Google Scholar 

Download references

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Authors and Affiliations

  1. Department of Computing, Mathematics, and Physics, Western Norway University of Applied Sciences (HVL), Bergen, Norway

    Jerry Chun-Wei Lin

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

    Yuyu Zhang

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

    Philippe Fournier-Viger

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

    Tzung-Pei Hong

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  1. Jerry Chun-Wei Lin
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  2. Yuyu Zhang
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  3. Philippe Fournier-Viger
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  4. Tzung-Pei Hong
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Correspondence to Jerry Chun-Wei Lin.

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Lin, J.CW., Zhang, Y., Fournier-Viger, P. et al. Efficiently Updating the Discovered Multiple Fuzzy Frequent Itemsets with Transaction Insertion. Int. J. Fuzzy Syst. 20, 2440–2457 (2018). https://doi.org/10.1007/s40815-018-0520-5

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  • DOI: https://doi.org/10.1007/s40815-018-0520-5

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