Multimedia Tools and Applications

, Volume 75, Issue 9, pp 4887–4912 | Cite as

Updating high-utility pattern trees with transaction modification

  • Chun-Wei Lin
  • Binbin Zhang
  • Wensheng Gan
  • Bo-Wei Chen
  • Seungmin Rho
  • Tzung-Pei Hong


Traditional association-rule mining only concerns the occurrence frequencies of the items in a binary database. In real-world applications, customers may buy several copies of the purchased items. Other factors such as profit, quantity, or price should be concerned to measure the utilities of the purchased items. High-utility itemsets mining was thus proposed to consider the factors of quantity and profit. Two-phase model was the most commonly way to keep the transaction-weighted utilization downward closure property, thus reducing the numerous candidates in utility mining. Most methods for finding high-utility itemsets are used to handle a static database. In practical applications, transactions are changed whether insertion, deletion, or modification. Some itemsets may arise as the new high-utility itemsets or become invalid knowledge in the updated database. In this paper, a maintenance Fast Updated High Utility Pattern tree for transaction MODification (FUP-HUP-tree-MOD) algorithm is thus proposed to effective maintain and update the built HUP tree for mining high-utility itemsets in dynamic databases without candidate generation. Experiments are conducted to show better performance of the proposed algorithm compared to the two-phase algorithm and the HUP tree algorithm in batch mode.


Modification Utility mining Two-phase algorithm Dynamic databases 



This research was partially supported by the Shenzhen Peacock Project, China, under grant KQC201109020055A, by the Natural Scientific Research Innovation Foundation in Harbin Institute of Technology under grant HIT.NSRIF.2014100, and by the Shenzhen Strategic Emerging Industries Program under grant ZDSY20120613125016389.


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Copyright information

© Springer Science+Business Media New York 2014

Authors and Affiliations

  • Chun-Wei Lin
    • 1
    • 2
  • Binbin Zhang
    • 3
  • Wensheng Gan
    • 1
  • Bo-Wei Chen
    • 4
  • Seungmin Rho
    • 5
  • Tzung-Pei Hong
    • 6
    • 7
  1. 1.Innovative Information Industry Research Center (IIIRC), School of Computer Science and TechnologyHarbin Institute of Technology Shenzhen Graduate SchoolShenzhenChina
  2. 2.Shenzhen Key Laboratory of Internet Information Collaboration, School of Computer Science and TechnologyHarbin Institute of Technology Shenzhen Graduate SchoolShenzhenChina
  3. 3.Medical SchoolShenzhen UniversityShenzhenChina
  4. 4.Department of Electrical EngineeringNational Cheng Kung UniversityTainanTaiwan
  5. 5.Department of MultimediaSungkyul UniversityAnyang-siSouth Korea
  6. 6.Department of Computer Science and Information EngineeringNational University of KaohsiungKaohsiungRepublic of China
  7. 7.Department of Computer Science and EngineeringNational Sun Yat-sen UniversityKaohsiungRepublic of China

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