Rare Correlated High Utility Itemsets Mining: An Experimental Approach

  • P. Lalitha Kumari
  • S. G. Sanjeevi
  • T. V. Madhusudhana Rao
Conference paper
Part of the Advances in Intelligent Systems and Computing book series (AISC, volume 710)


High utility itemsets are having utility more than user-specified minimum utility. These itemsets provide high profit but do not exhibit correlation between them. High utility itemsets mining generate huge number of itemsets considering only single interesting criteria. Existing algorithm mines correlated high utility itemsets mining extracts itemsets that provide high utility with correlation between them. The limitation of this algorithm is that it does not consider the rarity of itemsets. To overcome this limitation, this proposed algorithm mines rare correlated high utility itemsets. Firstly, it mines correlated high utility itemsets. Secondly, it determines whether the itemsets support is no greater than minsup specified by the user. It can be shown by experimental results that the proposed algorithm reduces considerably runtime and number of candidate itemsets.


Correlated itemsets Rare itemsets Correlated high utility itemsets Frequent itemsets 


  1. 1.
    Agrawal R, Srikant R, Fast algorithms for mining association rules. In: 20th international conference on very large databases, (1994) pp. 487–499.Google Scholar
  2. 2.
    Ahmed CF, Tanbeer SK, Jeong B, Lee Y, Efficient tree structures for high utility pattern mining in incremental databases. IEEE Transactions on Knowledge and Data Engineering. (2009). pp. 1708–1721.Google Scholar
  3. 3.
    Bai-En Shie, Philip.S.Yu, Vincent.S.Tseng, Efficient algorithms for mining maximal high utility itemsets from data streams with different models, Expert Systems with Applications (2012), pp. 12947–12960.Google Scholar
  4. 4.
    Barsky, M., Kim, S., Weninger, T., Han, J., Mining Flipping correlations from large datasets with taxonomies. In: Proc. 38th Int. Conf. on Very Large Databases. (2012). pp. 370–381.Google Scholar
  5. 5.
    Ben Younes, N., Hamrouni, T., Ben Yahia, S.: Bridging conjunctive and disjunctive search spaces for mining a new concise and exact representation of correlated patterns. In: Proc. 13th Int. Conf. Discovery Science. (2010). pp. 189–204.Google Scholar
  6. 6.
    Fournier-Viger P., Lin J.CW., Dinh T., Le H.B. Mining Correlated High-Utility Itemsets Using the Bond Measure. In: Martínez-Álvarez F., Troncoso A., Quintián H., Corchado E. (eds) Hybrid Artificial Intelligent Systems. HAIS 2016. Springer. (2016).Google Scholar
  7. 7.
    Grahne G, Zhu J, Fast algorithms for frequent itemset mining using FP-Trees. IEEE Transactions on Knowledge and Data Engineering. (2005). pp. 1347–1362.Google Scholar
  8. 8.
    Han J, Pei J, Yin Y, Mining frequent itemsets without candidate generation. In: Proc. of the 2000 ACM SIGMOD int’l conf. on management of data. (2000). pp. 1–12.Google Scholar
  9. 9.
    Hu Y, Chen Y, Mining association rules with multiple minimum supports: a new mining algorithm and a support tuning mechanism. Decision Support Systems. (2006). pp. 1–24.Google Scholar
  10. 10.
    Huynh-Thi-Le Q, Le T, Vo B, Le HBAn efficient and effective algorithm for mining top-rank-k frequent itemsets. Expert Systems Applications. (2015). pp. 156–164.Google Scholar
  11. 11.
    Kiran RU, Reddy PK, An improved multiple minimum support based approach to mine rare association rules. CIDM 2009. (2009). pp. 340–347.Google Scholar
  12. 12.
    Lee G, Yun U, Ryu K, Sliding window based weighted maximal frequent itemsets mining over data streams. Expert Systems Applications. (2014). pp. 694–708.Google Scholar
  13. 13.
    Lee G, Yun U, Ryang H, An Uncertainty-based Approach: Frequent Itemset Mining from Uncertain Data with Different Item Importance. Knowledge-Based Systems. (2015). pp. 239–256.Google Scholar
  14. 14.
    Lin, J. C.-W., Gan, W., Fournier-Viger, P., Hong, T.-P, Mining Discriminative High Utility Patterns. Proc. 8th Asian Conference on Intelligent Information and Database Systems. Springer. (2016).Google Scholar
  15. 15.
    Liu Y, Liao W, Choudhary A, A two-phase algorithm for fast discovery of high utility itemsets. Advanced Knowledge Discovery in Data Mining. (2005). pp. 689–695.Google Scholar
  16. 16.
    Mengchi Liu, Junfeng Qu, Mining High Utility Itemsets without Candidate Generation, Proceedings of the 21st ACM international conference on Information and knowledge management, (2012), pp. 55–64.Google Scholar
  17. 17.
    Sahoo, J., Das, A.K. & Goswami, A. An efficient fast algorithm for discovering closed+ high utility itemsets, Applied Intelligence (2016), pp. 44–74.Google Scholar
  18. 18.
    Soulet, A., Raissi, C., Plantevit, M., Cremilleux, B.: Mining dominant patterns in the sky. In: Proc. 11th IEEE International Conference on Data Mining. (2011). pp. 655–664.Google Scholar
  19. 19.
    Tempaiboolkul J, Mining rare association rules in a distributed environment using multiple minimum supports. ICIS 2013. (2013). pp. 295–299.Google Scholar
  20. 20.
    Tseng VS, Wu CW, Shie BE, Yu PS, UP-Growth: an efficient algorithm for high utility itemset mining. In: Proc. of the 16th ACM SIGKDD int’l conf. on knowledge discovery and data mining (KDD 2010). (2010). pp. 253–262.Google Scholar
  21. 21.
    Weng CH, Mining fuzzy specific rare itemsets for education data. Knowledge-Based Systems. (2011). pp. 697–708.Google Scholar
  22. 22.
    Xu T, Dong X, Mining frequent itemsets with multiple minimum supports using basic Apriori. ICNC 2013. (2013). pp. 957–961.Google Scholar
  23. 23.
    Yun U, Yoon E, An efficient approach for mining weighted approximate closed frequent patterns considering noise constraints, International Journal of Uncertainty Fuzziness Knowledge Based Systems. (2014). pp. 879–912.Google Scholar

Copyright information

© Springer Nature Singapore Pte Ltd. 2018

Authors and Affiliations

  • P. Lalitha Kumari
    • 1
  • S. G. Sanjeevi
    • 1
  • T. V. Madhusudhana Rao
    • 2
  1. 1.Department of CSENational Institute of TechnologyWarangalIndia
  2. 2.Department of CSESri Sivani College of EngineeringSrikakulamIndia

Personalised recommendations