Incrementally mining high utility itemsets in dynamic databases

Chun Wei Lin, Tzung Pei Hong, Guo Cheng Lan, Hsin Yi Chen, Hung Yu Kao

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

Utility mining is proposed to consider additional measures, such as profits or costs according to user preference. In the past, a two-phase mining algorithm was proposed for fast discovering high utility itemsets from databases. In this paper, an incremental mining algorithm to efficiently update high utility itemsets is proposed for record insertion. Experimental results also show that the proposed algorithm executes faster than the two-phase batch mining algorithm.

Original languageEnglish
Title of host publicationProceedings - 2010 IEEE International Conference on Granular Computing, GrC 2010
Pages303-307
Number of pages5
DOIs
Publication statusPublished - 2010 Nov 1
Event2010 IEEE International Conference on Granular Computing, GrC 2010 - San Jose, CA, United States
Duration: 2010 Aug 142010 Aug 16

Publication series

NameProceedings - 2010 IEEE International Conference on Granular Computing, GrC 2010

Other

Other2010 IEEE International Conference on Granular Computing, GrC 2010
CountryUnited States
CitySan Jose, CA
Period10-08-1410-08-16

All Science Journal Classification (ASJC) codes

  • Computational Theory and Mathematics
  • Computer Science Applications

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  • Cite this

    Lin, C. W., Hong, T. P., Lan, G. C., Chen, H. Y., & Kao, H. Y. (2010). Incrementally mining high utility itemsets in dynamic databases. In Proceedings - 2010 IEEE International Conference on Granular Computing, GrC 2010 (pp. 303-307). [5575938] (Proceedings - 2010 IEEE International Conference on Granular Computing, GrC 2010). https://doi.org/10.1109/GrC.2010.151