Mining High Utility Itemsets Based on the Pre-large Concept

  • Chun-Wei Lin
  • Tzung-Pei Hong
  • Guo-Cheng Lan
  • Jia-Wei Wong
  • Wen-Yang Lin
Part of the Smart Innovation, Systems and Technologies book series (SIST, volume 20)


In this paper, an incremental mining algorithm is proposed for efficiently maintaining the discovered high utility itemsets based on the pre-large concept. It first partitions itemsets into nine cases according to whether they are large (high), pre-large or small transaction-weighted utilization in the original database and in the inserted transactions. Each part is then performed by its own procedure. Experimental results also show that the designed incremental high utility mining algorithm has better performance than the bach one for handling inserted transactions.


Utility mining pre-large itemset high utility itemset incremental mining two-phase approach 


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  1. 1.
    Agrawal, R., Srikant, R.: Fast algorithms for mining association rules in large databases. In: The International Conference on Very Large Data Bases, pp. 487–499 (1994)Google Scholar
  2. 2.
    Berzal, F., Cubero, J.C., Marín, N., Serrano, J.M.: TBAR: An efficient method for association rule mining in relational databases. Data and Knowledge Engineering 37, 47–64 (2001)MATHCrossRefGoogle Scholar
  3. 3.
    Chan, R., Yang, Q., Shen, Y.D.: Mining high utility itemsets. In: IEEE International Conference on Data Mining, pp. 19–26 (2003)Google Scholar
  4. 4.
    Chen, M.S., Han, J., Yu, P.S.: Data mining: an overview from a database perspective. IEEE Transactions on Knowledge and Data Engineering 8, 866–883 (1996)CrossRefGoogle Scholar
  5. 5.
    Cheung, D.W., Han, J., Ng, V.T., Wong, C.Y.: Maintenance of discovered association rules in large databases: an incremental updating technique. In: The International Conference on Data Engineering, pp. 106–114 (1996)Google Scholar
  6. 6.
    Hong, T.P., Wang, C.Y., Tao, Y.H.: A new incremental data mining algorithm using pre-large itemsets. Intelligent Data Analysis 5, 111–129 (2001)MATHGoogle Scholar
  7. 7.
    Hong, T.P., Wang, C.Y.: Maintenance of association rules using pre-large itemsets. In: Intelligent Databases: Technologies and Applications, pp. 44–60 (2006)Google Scholar
  8. 8.
    IBM quest data mining project, Quest synthetic data generation code,
  9. 9.
    Lin, C.W., Hong, T.P., Lu, W.H.: The Pre-FUFP algorithm for incremental mining. Expert Systems with Applications 36, 9498–9505 (2009)CrossRefGoogle Scholar
  10. 10.
    Lin, C.W., Lan, G.C., Hong, T.P.: An incremental mining algorithm for high utility itemsets. Expert Systems with Applications 39, 7173–7180 (2012)CrossRefGoogle Scholar
  11. 11.
    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
  12. 12.
    Yao, H., Hamilton, H.J.: Mining itemset utilities from transaction databases. Data and Knowledge Engineering 59, 603–626 (2006)CrossRefGoogle Scholar
  13. 13.
    Yao, H., Hamilton, H.J., Butz, C.J.: A foundational approach to mining itemset utilities from databases. In: The SIAM International Conference on Data Mining, pp. 211–225 (2004)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2013

Authors and Affiliations

  • Chun-Wei Lin
    • 1
  • Tzung-Pei Hong
    • 2
    • 3
  • Guo-Cheng Lan
    • 4
  • Jia-Wei Wong
    • 3
  • Wen-Yang Lin
    • 2
  1. 1.School of Computer Science and Technology, Harbin Institute of Technology Shenzhen Graduate SchoolInnovative Information Industry Research Center (IIIRC)XiliP.R. China
  2. 2.Department of Computer Science and Information EngineeringNational University of KaohsiungKaohsiungTaiwan, R.O.C.
  3. 3.Department of Computer Science and EngineeringNational Sun Yat-sen UniversityKaohsiungTaiwan, R.O.C.
  4. 4.Department of Computer Science and Information EngineeringNational Cheng Kung UniversityTainanTaiwan, R.O.C.

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