HURI – A Novel Algorithm for Mining High Utility Rare Itemsets

  • Jyothi Pillai
  • O. P. Vyas
  • Maybin Muyeba
Part of the Advances in Intelligent Systems and Computing book series (AISC, volume 177)


In Data mining field, the primary task is to mine frequent itemsets from a transaction database using Association Rule Mining (ARM). Utility Mining aims to identify itemsets with high utilities by considering profit, quantity, cost or other user preferences. In market basket analysis, high consideration should be given to utility of item in a transaction, since items having low selling frequencies may have high profits. As a result, High Utility Itemset Mining emerged as a revolutionary field in Data Mining. Rare itemsets provide useful information in different decision-making domains. High Utility Rare Itemset Mining, HURI algorithm proposed in [12], generate high utility rare itemsets of users’ interest. HURI is a two-phase algorithm, phase 1 generates rare itemsets and phase 2 generates high utility rare itemsets, according to users’ interest. In this paper, performance evaluation and complexity analysis of HURI algorithm, based on different parameters have been discussed which indicates the efficiency of HURI.


Association Rule Mining Utility Mining Rare itemset High Utility Rare itemset Mining 


Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.


  1. 1.
    Pillai, J., Vyas, O.P.: User centric approach to itemset utility mining in Market Basket Analysis. IJCSE 3(1), 393–400 (2011) ISSN : 0975-3397Google Scholar
  2. 2.
    Pillai, J., Vyas, O.P.: Overview of Itemset Utility Mining and its Applications. IJCA (0975–8887) 5(1), 9–13 (2010)CrossRefGoogle Scholar
  3. 3.
    Pillai, J., Vyas, O.P., Soni, S., Muyeba, M.: A Conceptual Approach to Temporal Weighted Itemset Utility Mining. IJCA (0975-8887) 1(28) (2010)Google Scholar
  4. 4.
    Shankar, S., Purusothoman, T.P., Jayanthi, S., Babu, N.: A Fast Algorithm for Mining High Utility Itemsets. In: Proceedings of IEEE IACC 2009, India, pp. 1459–1464 (2009)Google Scholar
  5. 5.
    Adda, M., Wu, L., Feng, Y.: Rare Itemset Mining. In: Sixth International Conference on Machine Learning and Applications, pp. 73–80 (2007)Google Scholar
  6. 6.
    Szathmary, L., Napoli, A., Valtchev, P.: Towards Rare Itemset Mining. In: Proceedings of the 19th IEEE International Conference on Tools with Artificial Intelligence, vol. 1, pp. 305–312 (2007) ISSN: 1082-3409 , 0-7695-3015-XGoogle Scholar
  7. 7.
    Erwin, A., Gopalan, R.P., Achuthan, N.R.: A Bottom-up Projection based Algorithm for mining high utility itemsets. In: Proceedings of AIDM 2007, Australia, Conferences in Research and Practice in Information Technology, CRPIT, vol. 84 (2007)Google Scholar
  8. 8.
    Yao, H., Hamilton, H., Geng, L.: A Unified Framework for Utilty-Based Measures for Mining Itemsets. In: Proceedings of UBDM, pp. 28–37 (2006)Google Scholar
  9. 9.
    Sun, X., Orlowska, M.E., Li, X.: Finding Temporal Features of Event-Oriented Patterns. In: Ho, T.-B., Cheung, D., Liu, H. (eds.) PAKDD 2005. LNCS (LNAI), vol. 3518, pp. 778–784. Springer, Heidelberg (2005)CrossRefGoogle Scholar
  10. 10.
    Agrawal, R., Imielinski, T., Swami, A.: Mining association rules between sets of items in large databases. In: Proceedings of the 1993 ACM SIGMOD International Conference on Management of Data, Washington, pp. 207–216 (1993)Google Scholar
  11. 11.
    Lan, G.C., Hong, T.P., Tseng, V.S.: A Novel Algorithm for Mining Rare-Utility Itemsets in a Multi-Database Environment. In: 26th Workshop on Combinatorial Mathematics and Computation Theory, pp. 293–302Google Scholar
  12. 12.
    Pillai, J., Vyas, O.P.: High Utility Rare Itemset Mining (HURI): An approach for extracting high-utility rare itemsets. Journal on Future Engineering and Technology 7(1) (October 1, 2011)Google Scholar
  13. 13.
    Data Mining: A Competitive Tool in the Bankingand Retail Industries,
  14. 14.
    Liu, Y., Liao, W.-k., Choudhary, A.K.: A Two-Phase Algorithm for Fast Discovery of High Utility Itemsets. In: Ho, T.-B., Cheung, D., Liu, H. (eds.) PAKDD 2005. LNCS (LNAI), vol. 3518, pp. 689–695. Springer, Heidelberg (2005)CrossRefGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2013

Authors and Affiliations

  1. 1.Bhilai Institute of TechnologyDurgIndia
  2. 2.Indian Institute of Information TechnologyAllahabadIndia
  3. 3.Deptt. of Computing and MathematicsManchester Metropolitan Univ.ManchesterU.K.

Personalised recommendations