FHM: Faster High-Utility Itemset Mining Using Estimated Utility Co-occurrence Pruning

  • Philippe Fournier-Viger
  • Cheng-Wei Wu
  • Souleymane Zida
  • Vincent S. Tseng
Part of the Lecture Notes in Computer Science book series (LNCS, volume 8502)


High utility itemset mining is a challenging task in frequent pattern mining, which has wide applications. The state-of-the-art algorithm is HUI-Miner. It adopts a vertical representation and performs a depth-first search to discover patterns and calculate their utility without performing costly database scans. Although, this approach is effective, mining high-utility itemsets remains computationally expensive because HUI-Miner has to perform a costly join operation for each pattern that is generated by its search procedure. In this paper, we address this issue by proposing a novel strategy based on the analysis of item co-occurrences to reduce the number of join operations that need to be performed. An extensive experimental study with four real-life datasets shows that the resulting algorithm named FHM (Fast High-Utility Miner) reduces the number of join operations by up to 95 % and is up to six times faster than the state-of-the-art algorithm HUI-Miner.


Frequent pattern mining high-utility itemset mining co-occurrence pruning transaction database 


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  1. 1.
    Agrawal, R., Srikant, R.: Fast algorithms for mining association rules in large databases. In: Proc. Int. Conf. Very Large Databases, pp. 487–499 (1994)Google Scholar
  2. 2.
    Ahmed, C.F., Tanbeer, S.K., Jeong, B.-S., Lee, Y.-K.: Efficient Tree Structures for High-utility Pattern Mining in Incremental Databases. IEEE Trans. Knowl. Data Eng. 21(12), 1708–1721 (2009)CrossRefGoogle Scholar
  3. 3.
    Fournier-Viger, P., Gomariz, A., Campos, M., Thomas, R.: Fast Vertical Mining Sequential Pattern Mining Using Co-occurrence Information. In: Tseng, V.S., Ho, T.B., Zhou, Z.-H., Chen, A.L.P., Kao, H.-Y. (eds.) PAKDD 2014, Part I. LNCS (LNAI), vol. 8443, pp. 40–52. Springer, Heidelberg (2014)CrossRefGoogle Scholar
  4. 4.
    Fournier-Viger, P., Wu, C.-W., Gomariz, A., Tseng, V.S.: VMSP: Efficient Vertical Mining of Maximal Sequential Patterns. In: Sokolova, M., van Beek, P. (eds.) Canadian AI. LNCS (LNAI), vol. 8436, pp. 83–94. Springer, Heidelberg (2014)CrossRefGoogle Scholar
  5. 5.
    Fournier-Viger, P., Nkambou, R., Tseng, V.S.: RuleGrowth: Mining Sequential Rules Common to Several Sequences by Pattern-Growth. In: Proc. ACM 26th Symposium on Applied Computing, pp. 954–959 (2011)Google Scholar
  6. 6.
    Li, Y.-C., Yeh, J.-S., Chang, C.-C.: Isolated items discarding strategy for discovering high utility itemsets. Data & Knowledge Engineering 64(1), 198–217 (2008)CrossRefGoogle Scholar
  7. 7.
    Liu, M., Qu, J.: Mining High Utility Itemsets without Candidate Generation. In: Proceedings of CIKM 2012, pp. 55–64 (2012)Google Scholar
  8. 8.
    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
  9. 9.
    Shie, B.-E., Cheng, J.-H., Chuang, K.-T., Tseng, V.S.: A One-Phase Method for Mining High Utility Mobile Sequential Patterns in Mobile Commerce Environments. In: Proceedings of IEA/AIE 2012, pp. 616–626 (2012)Google Scholar
  10. 10.
    Tseng, V.S., Shie, B.-E., Wu, C.-W., Yu, P.S.: Efficient Algorithms for Mining High Utility Itemsets from Transactional Databases. IEEE Trans. Knowl. Data Eng. 25(8), 1772–1786 (2013)CrossRefGoogle Scholar
  11. 11.
    Yin, J., Zheng, Z., Cao, L.: USpan: An Efficient Algorithm for Mining High Utility Sequential Patterns. In: Proceedings of ACM SIG KDD 2012, pp. 660–668 (2012)Google Scholar

Copyright information

© Springer International Publishing Switzerland 2014

Authors and Affiliations

  • Philippe Fournier-Viger
    • 1
  • Cheng-Wei Wu
    • 2
  • Souleymane Zida
    • 1
  • Vincent S. Tseng
    • 2
  1. 1.Dept. of Computer ScienceUniversity of MonctonCanada
  2. 2.Dept. of Computer Science and Information EngineeringNational Cheng Kung UniversityTaiwan

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