Finding maximal frequent itemsets over online data streams adaptively

Daesu Lee, Won Suk Lee

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

59 Citations (Scopus)

Abstract

Due to the characteristics of a data stream, it is very important to confine the memory usage of a data mining process regardless of the amount of information generated in the data stream. For this purpose, this paper proposes a CP-tree (Compressed-prefix tree) that can be effectively used in finding either frequent or maximal frequent itemsets over an online data stream. Unlike a prefix tree, a node of a CP-tree can maintain the information of several item-sets together. Based on this characteristic, the size of a CP-tree can be flexibly controlled by merging or splitting nodes. In this paper, a mining method employing a CP-tree is proposed and an adaptive memory utilization scheme is also presented in order to maximize the mining accuracy of the proposed method for confined memory space at all times. Finally, the performance of the proposed method is analyzed by a series of experiments to identify its various characteristics.

Original languageEnglish
Title of host publicationProceedings - Fifth IEEE International Conference on Data Mining, ICDM 2005
Pages266-273
Number of pages8
DOIs
Publication statusPublished - 2005 Dec 1
Event5th IEEE International Conference on Data Mining, ICDM 2005 - Houston, TX, United States
Duration: 2005 Nov 272005 Nov 30

Publication series

NameProceedings - IEEE International Conference on Data Mining, ICDM
ISSN (Print)1550-4786

Other

Other5th IEEE International Conference on Data Mining, ICDM 2005
CountryUnited States
CityHouston, TX
Period05/11/2705/11/30

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All Science Journal Classification (ASJC) codes

  • Engineering(all)

Cite this

Lee, D., & Lee, W. S. (2005). Finding maximal frequent itemsets over online data streams adaptively. In Proceedings - Fifth IEEE International Conference on Data Mining, ICDM 2005 (pp. 266-273). [1565688] (Proceedings - IEEE International Conference on Data Mining, ICDM). https://doi.org/10.1109/ICDM.2005.68