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Mining fuzzy high average-utility itemsets using fuzzy utility lists and efficient pruning approach

  • Fuzzy systems and their mathematics
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Abstract

Fuzzy high average-utility itemset (FHAUI) mining problem considers the effect of the length of the itemsets on their calculated utilities, in addition to the number of occurrences of each item in each transaction and the unit profit of each item as the internal and external utility of that item, respectively. FHAUI mining avoids generating large fuzzy high utility itemsets which consist of fuzzy low utility items. The fuzzy theory has been combined with the high average-utility itemset mining problem in order to better understanding the users' results and provide more useful information, such as determining the overlapping range of the set of discovered items. In this paper, by extending MHAI method (Yun and Kim in Future Gener Comp Syst 68:346–36, 2017) for fuzzy itemset using the fuzzy approach of (Lan et al. in Appl Soft Comput 30:767–777, 2015), a method called HiFAM is presented for efficient exploration of FHAUIs. The algorithm introduces a fuzzy average-utility list (FAUL) structure, by extending HAI-list from Yun and Kim (Future Gener Comp Syst 68:346–36, 2017), to summarize required information of the dataset in a compact form to explore the FHAUIs without candidate generation. After creating the FAUL of the items (1-itemsets), there is no need to re-scan the database in the proposed algorithm, and all the FAULs of the (k + 1)-itemsets can be obtained from the combination of the FAULs of k-itemsets and (k − 1)-itemsets. The complete set of all FHAUIs can be extracted by HiFAM, through a depth first exploration process. A pruning technique is also used in the proposed method to prevent the exploration of unpromising itemsets; their exploration does not lead to discover FHAUIs. This pruning strategy effectively reduces the memory consumption and time complexity of the proposed method. Various experiments are conducted using real and synthetic datasets, the results of which show the efficiency of the proposed method.

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References

  • Agrawal R, Srikant R (1994) Fast algorithms for mining association rules. In: Proc. 20th int conf very large data bases, VLDB'94. pp 487–499

  • Ahmed CF, Tanbeer SK, Jeong BS, Lee YK (2009) Efficient tree structures for high utility pattern mining in incremental databases. IEEE T Knowl Data En 21(12):1708–1721

    Google Scholar 

  • Alcalá-Fdez J, Alcalá R, Gacto MJ, Herrera F (2009) Learning the membership function contexts for mining fuzzy association rules by using genetic algorithms. Fuzzy Sets Syst 160(7):905–921

    MathSciNet  MATH  Google Scholar 

  • Bai A, Deshpande PS, Dhabu M (2018) Selective database projections based approach for mining high-utility itemsets. IEEE Access 6:14389–14409

    Google Scholar 

  • Cao L (2010) Domain-driven data mining: Challenges and prospects. IEEE T Knowl Data En 22(6):755–769

    Google Scholar 

  • Cao L, Zhao Y, Zhang H, Luo D, Zhang C, Park EK (2010) Flexible frameworks for actionable knowledge discovery. IEEE T Knowl Data En 22(9):1299–1312

    Google Scholar 

  • Cao L, Dong X, Zheng Z (2016) e-NSP: efficient negative sequential pattern mining. Artif Intell 235:156–182

    MathSciNet  MATH  Google Scholar 

  • Chan KC, Au WH (1997a) An effective algorithm for mining interesting quantitative association rules. In: proc 1997a ACM symposium on applied computing, ACM

  • Chan KC, Au WH (1997b) Mining fuzzy association rules. In: proc 6th int conf Information and knowledge management, ACM

  • Chan R, Yang Q, Shen YD (2003) Mining high utility itemsets. In Proc. 3rd IEEE Int. Conf. Data Mining, 2003, (Washington, D.C., USA, 2003). pp 19–22

  • Chen CH, Hong TP, Tseng VS (2009) An improved approach to find membership functions and multiple minimum supports in fuzzy data mining. Expert Syst Appl 36(6):10016–10024

    Google Scholar 

  • Chen CH, Hong TP, Tseng VS (2012) Fuzzy data mining for time-series data. Appl Soft Comput 12(1):536–542

    Google Scholar 

  • Chen CH, Li AF, Lee YC (2014) Actionable high-coherent-utility fuzzy itemset mining. Soft Comput 18(12):2413–2424

    Google Scholar 

  • Dawar S, Goyal V, Bera D (2017) A hybrid framework for mining high-utility itemsets in a sparse transaction database. Appl Intell 47:809–827

    Google Scholar 

  • Deng ZH (2016) DiffNodesets: an efficient structure for fast mining frequent itemsets. Appl Soft Comput 41:214–223

    Google Scholar 

  • Deng ZH, Lv SL (2014) Fast mining frequent itemsets using nodesets. Expert Syst Appl 41(10):4505–4512

    Google Scholar 

  • Deng ZH, Wang ZH (2010) A new fast vertical method for mining frequent itemsets. Int J Comput Int Syst 3(6):733–744

    Google Scholar 

  • Deng ZH, Wang ZH, Jiang JJ (2012) A new algorithm for fast mining frequent itemsets using n-lists. Sci China Inform Sci 55(9):2008–2030

    MathSciNet  MATH  Google Scholar 

  • Erwin A, Gopalan RP, Achuthan NR (2007) A bottom-up projection based algorithm for mining high utility itemsets. In: Proc. 2nd Int. Workshop Integrating artificial intelligence and data mining, 2007, (Gold Coast, Australia, 2007), vol 84. pp 3–11

  • Erwin A, Gopalan RP, Achuthan NR (2008) Efficient mining of high utility itemsets from large datasets, Pacific-Asia Conf. Advances in knowledge discovery and data mining, 2008, (Osaka, Japan, 2008), In: Washio T, Suzuki E, Ting KM, Inokuchi A (eds) Advances in knowledge discovery and data mining. PAKDD 2008. LNCS, vol 5012. Springer, Berlin, Heidelberg. pp 554–561

  • Fournier-Viger P, Wu CW, Zida S, Tseng VS (2014) FHM: faster high-utility itemset mining using estimated utility co-occurrence pruning. In: Int. symposium on methodologies for intelligent systems, (Roskilde, Denmark, 2013), In: Andreasen T, Christiansen H, Cubero JC, Raś ZW (eds) Foundations of intelligent systems. ISMIS, LNCS, vol 8502. Springer, Cham, pp 83–92

  • Gan W, Lin JCW, Fournier-Viger P, Chao HC, Tseng VS, Yu P (2019) A survey of utility-oriented pattern mining. IEEE T Knowl Data En. https://doi.org/10.1109/TKDE.2019.2942594

    Article  Google Scholar 

  • Gyenesei A (2001) A fuzzy approach for mining quantitativeassociation rules. Acta Cybern 15(2):305–320

    MathSciNet  MATH  Google Scholar 

  • Han J, Pei J, Yin Y (2000) Mining frequent patterns without candidate generation. In: Proc. ACM SIGMOD Int. Conf. Management of data, 1993 (Dallas, TX, USA, 2000), ACM SIGMOD Record 29. pp 1–12

  • Hong TP, Kuo CS, Chi SH (1999) Mining association rules from quantitative data. Intell Data Anal 3(5):363–376

    MATH  Google Scholar 

  • Hong TP, Lin KY, Wang SL (2003) Fuzzy data mining for interesting generalized association rules. Fuzzy Sets Syst 138(2):255–269

    MathSciNet  Google Scholar 

  • Hong TP, Kuo CS, Chi SH (2004) A fuzzy AprioriTid mining algorithm with reduced computational time. Appl Soft Comput 5(1):1–10

    Google Scholar 

  • Hong TP, Lee CH, Wang SL (2011) Effective utility mining with the measure of average utility. Expert Syst Appl 38(7):8259–8265

    Google Scholar 

  • Hong TP, Chiang MJ, Wang SL (2002) Mining from quantitative data with linguistic minimum supports and confidences. Fuzzy systems, 2002. FUZZ-IEEE’02. In: Proceedings of the 2002 IEEE international conference on, IEEE.3

  • Hong TP, Lin CW, Lin TC, Wang SL (2012) Incremental multiple fuzzy frequent pattern tree. In: Proc IEEE int conf fuzzy systems (FUZZ-IEEE)

  • Huang TCK (2012) Mining the change of customer behavior in fuzzy time-interval sequential patterns. Appl Soft Comput 12(3):1068–1086

    Google Scholar 

  • Krishnamoorthy S (2015) Pruning strategies for mining high utility itemsets. Expert Syst Appl 42(5):2371–2381

    Google Scholar 

  • Kuok CM, Fu A, Wong MH (1998) Mining fuzzy association rules in databases. ACM SIGMOD Rec 27(1):41–46

    Google Scholar 

  • Lai CP, Chung PC, Tseng VS (2010) A novel algorithm for mining fuzzy high utility itemsets. Int J Innov Comput Inf Control (ICIC) 6(10):4347–4361

    Google Scholar 

  • Lan GC, Hong TP, Tseng VS (2012a) Efficiently mining high average-utility itemsets with an improved upper-bound strategy. Int J Inf Tech Decis 11(05):1009–1030

    Google Scholar 

  • Lan GC, Hong TP, Tseng VS (2012b) A projection-based approach for discovering high average-utility itemsets. J Inf Sci Eng 28(1):193–209

    Google Scholar 

  • Lan GC, Hong TP, Tseng VS (2014) An efficient projection-based indexing approach for mining high utility itemsets. Knowl Inf Syst 38:85–107

    Google Scholar 

  • Lan GC, Hong TP, Lin YH, Wang SL (2015) Fuzzy utility mining with upper-bound measure. Appl Soft Comput 30:767–777

    Google Scholar 

  • Lee YC, Hong TP, Wang TC (2008) Multi-levelfuzzy mining with multiple minimum supports. Expert Syst Appl 34(1):459–468

    Google Scholar 

  • Lee H, Shao B, Kang U (2015) Fast graph mining with HBase. Inf Sci 315:56–66

    MathSciNet  Google Scholar 

  • Li YC, Yeh JS, Chang CC (2008) Isolated items discarding strategy for discovering high utility itemsets. Data Knowl Eng 64:198–217

    Google Scholar 

  • Lin CW, Hong TP (2014) Mining fuzzy frequent itemsets based on UBFFP trees. J Intell Fuzzy Syst 27(1):535–548

    MathSciNet  Google Scholar 

  • Lin JCW, Hong TP, Lu WH (2010a) Linguistic data mining with fuzzy FP-trees. Expert Syst Appl 37(6):4560–4567

    Google Scholar 

  • Lin JCW, Hong TP, Lu WH (2010b) An efficient tree-based fuzzy data mining approach. Int J Fuzzy Syst 12(2):150–157

    Google Scholar 

  • Lin JCW, Hong TP, Lu WH (2011) An effective tree structure for mining high utility itemsets. Expert Syst Appl 38(6):7419–7424

    Google Scholar 

  • Lin JCW, Hong TP, Lin TC (2015) A CMFFP-tree algorithm to mine complete multiple fuzzy frequent itemsets. Appl Soft Comput 28:431–439

    Google Scholar 

  • Lin JCW, Ren S, Fournier-Viger P, Hong TP (2017) EHAUPM: efficient high average-utility pattern mining with tighter upper bounds. IEEE Access 5:12927–12940

    Google Scholar 

  • Liu M, Qu J (2012) Mining high utility itemsets without candidate generation. In: Proc. 21st ACM Int. Conf. Information and knowledge management, Maui, Hawaii, USA. pp 55–64

  • Liu Y, Liao WK, Choudhary A (2005) A two-phase algorithm for fast discovery of high utility itemsets. In: Ho T, Cheung D, Liu H (eds) Pacific-Asia Conf. Advances in knowledge discovery and data mining, 2005, Hanoi, Vietnam. Advances in knowledge discovery and data mining 2005, LCNS, vol 3518, Springer, Heidelberg. pp 689–695

  • Liu L, Wang K, Fung BCM (2012) Direct discovery of high utility itemsets without candidate generation. In: Proc. 12th IEEE Int. Conf. Data mining, 2012, Washington, D.C., USA. pp 984–989

  • Lu T, Vo B, Nguyen HT, Hong TP (2014) A new method for mining high average utility itemsets. In: Saeed K, Snášel V (eds) computer information systems and industrial management. CISIM 2015. In: Lecture notes in computer science, vol 8838. Springer, Berlin, Heidelberg

  • Papadimitriou S, Mavroudi S (2005) The fuzzy frequent pattern tree. In: Proc 9th WSEAS int conf computers, ICCOMP'05. pp 1–7

  • IBM Quest Data Mining Projection, Quest synthetic data generation code (1996) Available at http://www.almaden.ibm.com/cs/quest/syndata.htm

  • Rahmati B, Sohrabi MK (2019) A systematic survey of high utility itemset mining. Int J Inf Tech Decis 18(4):1113–1185

    Google Scholar 

  • Ryang H, Yun U (2017) Indexed list-based high utility pattern mining with utility upper-bound reduction and pattern combination techniques. Knowl Inf Syst 51:627–659

    Google Scholar 

  • Sim ATH, Indrawan M, Zutshi S, Srinivasan B (2010) Logic-based pattern discovery. IEEE T Knowl Data En 22(6):798–811

    Google Scholar 

  • Sohrabi MK (2018) Gossip-based information fusion protocol for distributed frequent itemset mining. Enterp Inf Syst-UK 12(6):674–694

    Google Scholar 

  • Sohrabi MK, Barforoush AA (2012) Efficient colossal pattern mining in high dimensional datasets. Knowl-Based Syst 33:41–52

    Google Scholar 

  • Sohrabi MK, Barforoush AA (2013) Parallel frequent itemset mining using systolic arrays. Knowl-Based Syst 37:462–471

    Google Scholar 

  • Sohrabi MK, Roshani R (2017) Frequent itemset mining using cellular learning automata. Comput Hum Behav 68:244–253

    Google Scholar 

  • Sohrabi MK, Ghods V (2014) Top-down vertical itemset mining. In: Proc. 6th Int. Conf. Graphic and image processing, ICGIP. pp 94431V–94431V7

  • Sohrabi MK, Ghods V (2016) CUSE: A novel cube-based approach for sequential pattern mining. In: Proc 4th int symposium on Computational and Business Intelligence (ISCBI), 2016, Olten, Switzerland. pp 186–190

  • Srikant R, Agrawal R (1996) Mining quantitative association rules in large relational tables. ACM SIGMOD Rec 25(2):1–12

    Google Scholar 

  • Tran MT, Vo LB, B, (2015) Combination of dynamic bit vectors and transaction information for mining frequent closed sequences efficiently. Eng Appl Artif Intel 38:183–189

    Google Scholar 

  • Truong T, Duong H, Le HB, Fournier-Viger P (2019) Efficient vertical mining of high average-utility itemsets based on novel upper-bounds. IEEE T Knowl Data En 31(2):301–314

    Google Scholar 

  • Tseng VS, Shie BE, Wu CW, Yu PS (2013) Efficient algorithms for mining high utility itemsets from transactional databases. IEEE T Knowl Data En 25:1772–1786

    Google Scholar 

  • Tseng VS, Wu CW, Shie BE, Yu PS (2010) UP-Growth: An efficient algorithm for high utility itemset mining. In Proc. 16th ACM SIGKDD Int. Conf. Knowledge discovery and data mining, 2010, Washington, D.C., USA. pp 253–262

  • Wang CM, Chen SH, Huang YF (2009) A fuzzy approach for mining high utility quantitative itemsets. In: 2009 IEEE int conf fuzzy systems, FUZZ-IEEE

  • Wu JMT, Lin JCW, Pirouz M, Fournier-Viger P (2018) TUB-HAUPM: tighter upper bound for mining high average-utility patterns. IEEE Access 6:18655–18669

    Google Scholar 

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

    Google Scholar 

  • Yue JS, Tseng E, Yeung D, Shi D (2000). Mining fuzzy association rules with weighted items. Systems. In: Proc 2000 IEEE int conf systems, man and cybernetics. pp 1906–1911

  • Yun U, Kim D (2017) Mining of high average-utility itemsets using novel list structure and pruning strategy. Future Gener Comp Sy 68:346–360

    Google Scholar 

  • Yun U, Lee G (2016) Incremental mining of weighted maximal frequent itemsets from dynamic databases. Expert Syst Appl 54:304–327

    Google Scholar 

  • Zida S, Fournier-Viger P, Lin JCW, Wu C, Tseng VS (2017) EFIM: a fast and memory efficient algorithm for high-utility itemset mining. Knowl Inf Syst 51:595–625

    Google Scholar 

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  1. Department of Computer Engineering, Semnan Branch, Islamic Azad University, Semnan, Iran

    Manijeh Hajihoseini & Mohammad Karim Sohrabi

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  1. Manijeh Hajihoseini
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Correspondence to Mohammad Karim Sohrabi.

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Manijeh Hajihosseini (first author) declares that she has no conflict of interest. Mohammad Karim Sohrabi (second author) declares that he has no conflict of interest.

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Hajihoseini, M., Sohrabi, M.K. Mining fuzzy high average-utility itemsets using fuzzy utility lists and efficient pruning approach. Soft Comput 26, 6063–6086 (2022). https://doi.org/10.1007/s00500-022-07123-7

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