Performance of IF-Postdiffset and R-Eclat Variants in Large Dataset

 
 
 
  • Abstract
  • Keywords
  • References
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  • Abstract


    Pattern mining refers to a subfield of data mining that uncovers interesting, unexpected, and useful patterns from transaction databases. Such patterns reflect frequent and infrequent patterns. An abundant literature has dedicated in frequent pattern mining and tremendous efficient algorithms for frequent itemset mining in the transaction database. Nonetheless, the infrequent pattern mining has emerged to be an interesting issue in discovering patterns that rarely occur in the transaction database. More researchers reckon that rare pattern occurrences may offer valuable information in knowledge data discovery process. The R-Eclat is a novel algorithm that determines infrequent patterns in the transaction database. The multiple variants in the R-Eclat algorithm generate varied performances in infrequent mining patterns. This paper proposes IF-Postdiffset as a new variant in R-Eclat algorithm. This paper also highlights the performance of infrequent mining pattern from the transaction database among different variants of the R-Eclat algorithm regarding its execution time.

     

     

     

  • Keywords


    Pattern mining; Itemset mining; Infrequent itemset mining; R-Eclat algorithm; Large dataset.

  • References


      [1] Djenouri Y, Djenouri D, Habbas Z & Belhadi A, “How to Exploit High Performance Computing in Population-Based Metaheuristics for Solving Association Rule Mining Problem”, Distrib. Parallel Databases, Vol.36, No.2, (2018), pp.369-397.

      [2] Yacoob I, Hashem IAT, Gani A, Mokhtar S, Ahed E, Anuar NB & Vasilakos AV, “Big Data: From Beginning to Future”, International Journal of Information Management, Vol.36, No.6, (2016), pp.1231-1247.

      [3] Agrawal R, Imielinski T & Swami A, “Mining Association Rules Between Sets Of Items In Large Databases”, ACM SIGMOD, Vol.22, No.2, (1993), pp.207-216.

      [4] Agrawal R & Srikant R, “Fast Algorithms For Mining Association Rules In Large Databases”, Proc. 20th International Conference on Very Large Data Bases (VLDB), September 12–15, Santiago de Chile, Chile, (1994), pp.487- 499.

      [5] Rahman A, Ezeife CI & Aggarwal AK, “WiFi miner: An Online Apriori-Infrequent Based Wireless Intrusion System”, Knowledge Discovery from Sensor Data, Lecture Notes in Computer Science, Vol.5840, Springer, Berlin, (2010), pp.76-93.

      [6] Liu B, Hsu W & Ma Y, “Mining association rules with multiple minimum supports”, Proc. ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, (1999), pp.337-341.

      [7] Tsang S, Koh YS & Dobbie G, “RP-tree: Rare Pattern Tree Mining”, DaWaK (Lecture Notes in Computer Science), Alfredo Cuzzocrea and Umeshwar Dayal (Eds.), Springer, Berlin, Vol.6862, (2011), pp.277-288.

      [8] Gupta A, Mittal A & Bhattacharya A, “Minimally Infrequent Itemset Mining Using Pattern-Growth Paradigm And Residual Trees”, CoRR abs/1207.4958, (2012).

      [9] Zaki MJ, Parthasarathy S, Ogihara M, Li W et al., “New algorithms for fast discovery of association rules”, Proc. ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, (1997), pp.283-286.

      [10] Zaki MJ & Gouda K, “Fast Vertical Mining Using Diffsets”, ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, (2003), pp.326-335.

      [11] Bakar W, Jalil MA, Man. M, Abdullah Z & Mohd F, “Postdiffset: an Eclat-like algorithm for frequent itemset mining”, International Journal of Engineering & Technology, Vol.7, No.2.28, (2018), pp.197-199.

      [12] Bakar W, Man M & Jusoh JA, “Comparative performance analysis of postdiffset in frequent vs. infrequent Itemset mining”, International Journal of Engineering & Technology, Vol.7, No.3.28, (2018), pp.144-148.

      [13] Trieu TA, Kunieda Y, “An Improvement For Declat Algorithm”, Proc. 6th International Conference on Ubiquitous Information Management and Communication, No.54, (2012).

      [14] Jusoh JA, Man M & Bakar W, “Mining Infrequent Patterns Using R-Eclat Algorithms”, Journal of Fundamental and Applied Sciences, Vol.24, No.3, (2018).

      [15] Jusoh JA & Man M, “Modifying iEclat Algorithm for Infrequent Patterns Mining”, Advanced Science Letters, Vol.24, No.3, (2018).

      [16] Han J, Pei J, Yin Y & Mao R, “Mining Frequent Patterns without Candidate Generation: A Frequent-Pattern Tree Approach”, Data Mining and Knowledge Discovery, Vol.8, (2004), pp.53-87.


 

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Article ID: 28241
 
DOI: 10.14419/ijet.v7i4.1.28241




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