Extracting Diverse Patterns with Unbalanced Concept Hierarchy

Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 8443)


The process of frequent pattern extraction finds interesting information about the association among the items in a transactional database. The notion of support is employed to extract the frequent patterns. Normally, in a given domain, a set of items can be grouped into a category and a pattern may contain the items which belong to multiple categories. In several applications, it may be useful to distinguish between the pattern having items belonging to multiple categories and the pattern having items belonging to one or a few categories. The notion of diversity captures the extent the items in the pattern belong to multiple categories. The items and the categories form a concept hierarchy. In the literature, an approach has been proposed to rank the patterns by considering the balanced concept hierarchy. In a real life scenario, the concept hierarchies are normally unbalanced. In this paper, we propose a general approach to calculate the rank based on the diversity, called drank, by considering the unbalanced concept hierarchy. The experiment results show that the patterns ordered based on drank are different from the patterns ordered based on support, and the proposed approach could assign the drank to different kinds of unbalanced patterns.


data mining association rules frequent patterns diversity diverse rank interestingness concept hierarchy algorithms 


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  1. 1.
    Han, J., Cheng, H., Xin, D., Yan, X.: Frequent pattern mining: current status and future directions. Data Min. Knowl. Discov. 15(1), 55–86 (2007)CrossRefMathSciNetGoogle Scholar
  2. 2.
    Agrawal, R., Srikant, R.: Fast algorithms for mining association rules. In: 20th Intl. Conf. on VLDB, pp. 487–499 (1994)Google Scholar
  3. 3.
    Zaki, M.J., Hsiao, C.-J.: Efficient algorithms for mining closed itemsets and their lattice structure. IEEE TKDE 17(4), 462–478 (2005)Google Scholar
  4. 4.
    Hu, T., Sung, S.Y., Xiong, H., Fu, Q.: Discovery of maximum length frequent itemsets. Information Sciences 178(1), 69–87 (2008)CrossRefMathSciNetGoogle Scholar
  5. 5.
    Minh, Q.T., Oyanagi, S., Yamazaki, K.: Mining the K-most interesting frequent patterns sequentially. In: Corchado, E., Yin, H., Botti, V., Fyfe, C. (eds.) IDEAL 2006. LNCS, vol. 4224, pp. 620–628. Springer, Heidelberg (2006)CrossRefGoogle Scholar
  6. 6.
    Wang, J., Han, J., Lu, Y., Tzvetkov, P.: TFP: an efficient algorithm for mining top-k frequent closed itemsets. IEEE TKDE 17(5), 652–663 (2005)Google Scholar
  7. 7.
    Hu, J., Mojsilovic, A.: High-utility pattern mining: A method for discovery of high-utility item sets. Pattern Recogn. 40(11), 3317–3324 (2007)CrossRefzbMATHGoogle Scholar
  8. 8.
    Somya, S., Uday Kiran, R., Krishna Reddy, P.: Discovering Diverse-Frequent Patterns in Transactional Databases. In: International Conference on Management of Data (COMAD 2011), Bangalore, India, pp. 69–78 (2011)Google Scholar
  9. 9.
    Srikant, R., Agrawal, R.: Mining generalized association rules. In: VLDB, Zurich, Switzerland, pp. 407–419 (1995)Google Scholar
  10. 10.
    Han, J., Fu, Y.: Mining multiple-level association rules in large databases. IEEE TKDE 11(5), 798–805 (1999)Google Scholar
  11. 11.
    Chen, Y., Xue, G.-R., Yu, Y.: Advertising keyword suggestion based on concept hierarchy. In: WSDM 2008, pp. 251–260. ACM, USA (2008)Google Scholar
  12. 12.
    Geng, L., Hamilton, H.J.: Interestingness measures for data mining: a survey. ACM Comput. Surv. 38(3), 1–32 (2006)CrossRefGoogle Scholar
  13. 13.
    Hilderman, R.J., Hamilton, H.J.: Knowledge Discovery and Measures of Interest. Kluwer Academic Publishers, Norwell (2001)CrossRefzbMATHGoogle Scholar
  14. 14.
    Zbidi, N., Faiz, S., Limam, M.: On mining summaries by objective measures of interestingness. Machine Learning 62, 175–198 (2006)CrossRefGoogle Scholar
  15. 15.
    Huebner, R.A.: Diversity-based interestingness measures for association rule mining. In: ASBBS 2009, Las Vegas (2009)Google Scholar
  16. 16.
    Brin, S., Motwani, R., Silverstein, C.: Beyond market baskets: Generalizing association rules to correlations. SIGMOD Rec. 26(2), 265–276 (1997)CrossRefGoogle Scholar
  17. 17.
    Liu, B., Hsu, W., Mun, L.-F., Lee, H.-Y.: Finding interesting patterns using user expectations. IEEE TKDE 11(6), 817–832 (1999)Google Scholar
  18. 18.
    McGarry, K.: A survey of interestingness measures for knowledge discovery. Knowl. Eng. Rev. 20, 39–61 (2005)CrossRefGoogle Scholar
  19. 19.
    Omiecinski, E.: Alternative interest measures for mining associations in databases. IEEE TKDE 15(1), 57–69 (2003)MathSciNetGoogle Scholar
  20. 20.

Copyright information

© Springer International Publishing Switzerland 2014

Authors and Affiliations

  1. 1.Centre of Data EngineeringInternational Institute of Information Technology-Hyderabad (IIIT-H)HyderabadIndia

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