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Top-K Miner: top-K identical frequent itemsets discovery without user support threshold

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Abstract

Frequent itemsets (FIs) mining is a prime research area in association rule mining. The customary techniques find FIs or its variants on the basis of either support threshold value or by setting two generic parameters, i.e., N (topmost itemsets) and \(K_\mathrm{{max}}\) (size of the itemsets). However, users are unable to mine the absolute desired number of patterns because they tune these approaches with their approximate parameters settings. We proposed a novel technique, top-K Miner that does not require setting of support threshold, N and \(K_\mathrm{{max}}\) values. Top-K Miner requires the user to specify only a single parameter, i.e., K to find the desired number of frequent patterns called identical frequent itemsets (IFIs). Top-K Miner uses a novel candidate production algorithm called join-FI algorithm. This algorithm uses frequent 2-itemsets to yield one or more candidate itemsets of arbitrary size. The join-FI algorithm follows bottom-up recursive technique to construct candidate-itemsets-search tree. Finally, the generated candidate itemsets are manipulated by the Maintain-Top-K_List algorithm to produce Top-K_List of the IFIs. The proposed top-K Miner algorithm significantly outperforms the generic benchmark techniques even when they are running with the ideal parameters settings.

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References

  1. Agrawal R, Imielinski T, Swami A (1993) Mining association rules between sets of items in large databases. In: Proceedings of the 1993 ACM-SIGMOD international conference on management of data (SIGMOD’93). Washington, DC, pp 207–216

  2. Grahne G, Zhu J (2003) High performance mining of maximal frequent itemsets. In: Proceeding of the 2003 SIAM international workshop on high performance data mining. pp 135–143

  3. Lee W, Stolfo SJ, Mok KW (2000) Adaptive intrusion detection: a data mining approach. Artif Intell Rev 14(6):533–567

    Article  MATH  Google Scholar 

  4. Pei J, Han J, Mortazavi-Asl B, Zhu H (2000) Mining access patterns efficiently from web logs. In: Proceeding of the 2000 Pacific-Asia conference on knowledge discovery and data mining. Kyoto, Japan, pp 396–407

  5. Holt JD, Chung SM (1999) Efficient mining of association rules in text databases. In: Proceeding of the 1999 international conference on Information and knowledge management. Kansas City, Missouri, pp 234–242

  6. Klemettinen M (1999) A knowledge discovery methodology for telecommunication network alarm databases. Ph.D. thesis, University of Helsinki

  7. Satou K, Shibayama G, Ono T, Yamamura Y, Furuichi E, Kuhara S, Takagi T (1997) Finding associations rules on heterogeneous genome data. In: Proceeding of the 1997 Pacific symposium on biocomputing (PSB’97). Hawaii, pp 397–408

  8. Bayardo RJ (1998) Efficiently mining long patterns from databases. In: Proceeding of the 1998 ACM-SIGMOD international conference on management of data (SIGMOD’98). Seattle, WA, pp 85–93

  9. Burdick D, Calimlim M, Flannick J, Gehrke J, Yiu T (2005) MAFIA: a maximal frequent itemset algorithm. IEEE Trans Knowl Data Eng 17(11):1490–1504

    Article  Google Scholar 

  10. Gouda K, Zaki MJ (2005) GenMax: an efficient algorithm for mining maximal frequent itemsets. Data Min Knowl Discov 11(3):1–20

    Article  MathSciNet  Google Scholar 

  11. Pasquier N, Bastide Y, Taouil R, Lakhal L (1999) Discovering frequent closed itemsets for association rules. In: Proceeding of the 7th international conference on database theory (ICDT’99). Jerusalem, Israel, pp 398–416

  12. Pei J, Han J, Mao R (2000) CLOSET: an efficient algorithm for mining frequent closed itemsets. In: Proceeding of the 2000 ACM-SIGMOD international workshop data mining and knowledge discovery (DMKD’00). Dallas, TX, pp 11–20

  13. Zaki MJ, Hsiao CJ (2002) CHARM: an efficient algorithm for closed itemset mining. In: Proceeding of the 2002 SIAM international conference on data mining (SDM’02). Arlington, VA, pp 457–473

  14. Borgelt C, Yang X, Nogales-Cadenas R, Carmona-Saez P, Pascual-Montano A (2011) Finding closed frequent item sets by intersecting transactions. In: Proceedings of the 2011 international conference on extending database technology (EDBT-11). Sweden, Uppsala, pp 367–376

  15. Hu T, Sung SY, Xiong H, Fu Q (2008) Discovery of maximum length frequent itemsets. Inf Sci Int J 178(1):69–87

    MathSciNet  Google Scholar 

  16. Zhu F, Yan X, Han J, Yu PS, Cheng H (2007) Mining colossal frequent patterns by core pattern fusion. In: Proceeding of the 2007 international conference on data engineering (ICDE’07). Istanbul, Turkey, pp 706–715

  17. Dabbiru M, Shashi M (2010) An efficient approach to colossal pattern mining. Int J Comput Sci Netw Secur (IJCSNS) 10(1):304–312

    Google Scholar 

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

    Article  Google Scholar 

  19. Han J, Pei J, Yin Y (2000) Mining frequent patterns without candidate generation. In: Proceeding of the 2000 ACM-SIGMOD international conference on management of data (SIGMOD’00). Dallas, TX, pp 1–12

  20. Han J, Cheng H, Xin D, Yan (2007) Frequent pattern mining—current status and future directions. Data Min Knowl Discov 15(1):55–86

    Article  MathSciNet  Google Scholar 

  21. Cheung YL, Fu AWC (2004) Mining frequent itemsets without support threshold: with and without item constraints. IEEE Trans Knowl Data Eng 16(9):1052–1069

    Article  Google Scholar 

  22. Fu AWC, Kwong RWW, Tang J (2000) Mining N-most interesting itemsets. In: Proceedings of the 2000 international symposium on methodologies for intelligent systems. pp 59–67

  23. Ngan SC, Lam T, Wong RCW, Fu AWC (2005) Mining N-most interesting itemsets without support threshold by the COFI-tree. Int J Bus Intell Data Min 1(1):88–106

    Article  Google Scholar 

  24. El-Hajj M, Zaïane OR (2003) COFI-tree mining: a new approach to pattern growth with reduced candidacy generation. In: Workshop on frequent itemset mining implementations (FIMI 2003) in conjunction with IEEE-ICDM

  25. Salam A, Khayal M (2011) Mining top-k frequent patterns without minimum support threshold. Knowl Inf Syst 30(1):112–142

    Google Scholar 

  26. Li Y, Lin Q, Li R, Duan D (2010) TGP: mining top-K frequent closed graph pattern without minimum support. In: Proceeding of the 2010 international conference on advanced data mining and applications (ADMA ’10). pp 537–548

  27. Xie Y, Yu PS (2010) Max-Clique: a top-down graph-based approach to frequent pattern mining. In: Proceeding of the 2010 IEEE international conference on data mining (ICDM ’10). pp 1139–1144

  28. Okubo Y, Haraguchi M (2012) Finding top-N colossal patterns based on clique search with dynamic update of graph. In: Proceeding of the 2012 international conference on formal concept analysis (ICFCA’12). Springer, pp 244–259

  29. Agrawal R, Mannila H, Srikant R, Toivonen H, Verkamo A (1996) Fast discovery of association rules. In: Fayyad UM, Piatetsky G, Smyth P, Uthurusamy R (eds) Advances in KDD. MIT press

  30. Holsheimer M, Kersten M, Mannila H, Toivonen H (1995) A perspective on database and data mining. In: Proceeding of the 1995 international conference on knowledge discovery and data mining (KDD’ 95). pp 150–155

  31. Zaki MJ, Gouda K (2003) Fast vertical mining using diffsets. In: Proceedings of the 2003 ACM-SIGKDD international conference on knowledge discovery and data mining (SIGKDD’03). Washington, pp 326–335

  32. Frequent itemset mining implementations repository. http://fimi.cs.helsinki.fi/

  33. Shen L, Shen H, Pritchard P, Topor R (1998) Finding the N largest itemsets. In: Proceedings of international conference on data mining. pp 211–222

  34. Quang TM, Oyanagi S, Yamazaki K (2006) ExMiner: an efficient algorithm for mining top-K frequent patterns, ADMA 2006, LNAI 4093. pp 436–447

  35. Wang J, Han J (2005) TFP: an efficient algorithm for mining top-K frequent closed itemsets. IEEE Trans Knowl Data Eng 17(5):652–664

    Article  Google Scholar 

  36. Hirate Y, Iwahashi E, Yamana H (2004) TF2P-growth: an efficient algorithm for mining frequent patterns without any thresholds. In: Proceedings of ICDM

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Authors and Affiliations

  1. Institute of Information Technology, Kohat University of Science and Technology, Kohat, Pakistan

    Saif-Ur-Rehman, Jawad Ashraf & Asad Habib

  2. Computer Science Department, Abasyn University, Peshawar, Pakistan

    Abdus Salam

Authors
  1. Saif-Ur-Rehman
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  2. Jawad Ashraf
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  3. Asad Habib
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  4. Abdus Salam
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Correspondence to Saif-Ur-Rehman.

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Saif-Ur-Rehman, Ashraf, J., Habib, A. et al. Top-K Miner: top-K identical frequent itemsets discovery without user support threshold. Knowl Inf Syst 48, 741–762 (2016). https://doi.org/10.1007/s10115-015-0907-7

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  • DOI: https://doi.org/10.1007/s10115-015-0907-7

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