Interesting Pattern Mining Using Item Influence

  • Subrata DattaEmail author
  • Kalyani Mali
  • Sourav Ghosh
  • Ruchi Singh
  • Sourav Das
Conference paper
Part of the Learning and Analytics in Intelligent Systems book series (LAIS, volume 3)


Interesting patterns are very much needed in mining of significant association rules which play a big role in knowledge discovery. Frequency based pattern mining techniques such as support often lead to the generation of huge number of patterns including the uninteresting ones with high dissociation. Though dissociation is used to distinguish between two patterns having equal support, but if both of support and dissociation are same then it becomes very difficult to distinguish them. To overcome these types of problem we have introduced a new method of pattern mining based on the concept of item influence. How many other distinct items have been appeared with an item throughout the dataset is referred to its item influence (ii). The proposed method includes three consecutive steps such as- (1) measurement of item influence for all of the items present in the database, (2) calculation of transaction influence (ti) for all of the transactions present in the database using item influence and (3) measurement of influential weights (iw) for all of the generated itemsets. Pruning is done based on the minimum threshold value corresponding to influential weights. Experimental analysis shows the effectiveness of the method.


Pattern mining Item influence Transaction influence Influential weight Dissociation 



This research was partially supported by the DST PURSE II program, Kalyani University, West Bengal, India.


  1. 1.
    Agarwal R, Imielinski T, Swami A (1993) Mining association rules between sets of items in large datasets. In: Proceedings ACM SIGMOD 1993, pp 207–216Google Scholar
  2. 2.
    Chee CH, Jaafar J, Aziz IA, Hasan MH, Yeoh W (2018) Algorithms for frequent itemset mining: a literature review. In: Artificial Intelligence Review, pp 1–19Google Scholar
  3. 3.
    Pal S, Bagchi A (2005) Association against dissociation: some pragmatic consideration for frequent Itemset generation under fixed and variable thresholds. ACM SIGKDD Explor 7(2):151–159CrossRefGoogle Scholar
  4. 4.
    Datta S, Bose S (2015) Mining and ranking association rules in support, confidence, correlation and dissociation framework. In: Proceedings of FICTA, AISC, vol 404, Durgapur, India, pp 141–152Google Scholar
  5. 5.
    Datta S, Bose S (2015) Discovering association rules partially devoid of dissociation by weighted confidence. In: Proceedings of IEEE ReTIS, Kolkata, India, pp 138–143Google Scholar
  6. 6.
    Datta S, Mali K (2017) Trust: a new objective measure for symmetric association rule mining in account of dissociation and null transaction. In: Proceedings of IEEE ICoAC, Chennai, India, pp 151–156Google Scholar
  7. 7.
    Han J, Pei J, Yin Y (2000) Mining frequent patterns without candidate generation. In: Proceeddings ACM SIGMOD, Dallas, USA, pp 1–12Google Scholar
  8. 8.
    Wu JM-T, Zhan J, Chobe S (2018) Mining association rules for low-frequency itemsets. PLoS ONE 13(7):e0198066CrossRefGoogle Scholar
  9. 9.
    Vo B, Coenen F, Le B (2013) A new method for mining frequent weighted itemsets based on WIT-trees. Expert Syst Appl 40:1256–1264CrossRefGoogle Scholar
  10. 10.
    Datta S, Bose S (2015) Frequent pattern generation in association rule mining using weighted support. In: proceedings of IEEE C3IT, Hooghly, India, pp 1–5Google Scholar
  11. 11.
    Datta S, Chakraborty S, Mali K, Banerjee S, Roy K, Chatterjee S, Chakraborty M, Bhattacharjee S (2017) Optimal usages of pessimistic association rules in cost effective decision making. In: Proceedings of IEEE Optronix, Kolkata, India, pp 1–5Google Scholar
  12. 12.
    Li YC, Yeh JS, Chang CC (2008) Isolated items discarding strategy for discovering high utility itemsets. Data Knowl Eng 64:198–217CrossRefGoogle Scholar
  13. 13.
    Bui H, Vo B, Nguyen H, Nguyen-Hoang TA, Hong TP (2018) A weighted N-list- based method for mining frequent weighted itemsets. Expert Syst Appl 96:388–405CrossRefGoogle Scholar
  14. 14.
    Lee G, Yun U, Ryu KH (2017) Mining frequent weighted itemsets without storing transaction IDs and generating candidates. Int J Uncertain, Fuzziness Knowl-Based Syst 25(1):111–144CrossRefGoogle Scholar
  15. 15.
    Annapoorna V, Rama Krishna Murty M, Hari Priyanka JSVS, Chittineni S (2018) Comparative analysis of frequent pattern mining for large data using FP-tree and CP-tree methods. In: Proceedings of the 6th FICTA, AISC, vol 701, pp 59–67, Bhubaneswar, IndiaGoogle Scholar
  16. 16.
    Preti G, Lissandrini M, Mottin D, Velegrakis Y (2018) Beyond frequencies: graph pattern mining in multi-weighted graphs. In: Proceedings of the 21st EDBT, pp 169–180Google Scholar
  17. 17.
    Tang L, Zhang L, Luo P, Wang M (2012) Incorporating occupancy into frequent pattern mining for high quality pattern recommendation. In: Proceedings of the 21st ACM CIKM 2012, Hawaii, USA, pp 75–84Google Scholar
  18. 18.
    Gan W, Lin JC-W, Fournier-Viger P, Chao H-C, Tseng VS, Yu PS (2018) A survey of utility-oriented pattern mining. arXiv preprint arXiv:1805.10511
  19. 19.
    Schaus P, Aoga JOR, Guns T (2017) CoverSize: a global constraint for frequency-based itemset mining. In: Proceedings of the international conference on principles and practice of constraint programming, LNCS, vol 10416, pp 529–546Google Scholar
  20. 20.
    Cheung Y-L, Fu AW-C (2004) Mining frequent itemsets without support threshold: with or without item constraints. In: IEEE TKDE, vol 16, no 9Google Scholar

Copyright information

© Springer Nature Switzerland AG 2020

Authors and Affiliations

  • Subrata Datta
    • 1
    Email author
  • Kalyani Mali
    • 1
  • Sourav Ghosh
    • 2
  • Ruchi Singh
    • 2
  • Sourav Das
    • 2
  1. 1.Kalyani UniversityKalyaniIndia
  2. 2.Neotia Institute of Technology, Management and ScienceSarishaIndia

Personalised recommendations