Finding recent frequent itemsets adaptively over online data streams

Joong Hyuk Chang, Won Suk Lee

Research output: Contribution to conferencePaper

258 Citations (Scopus)

Abstract

A data stream is a massive unbounded sequence of data elements continuously generated at a rapid rate. Consequently, the knowledge embedded in a data stream is more likely to be changed as time goes by. Identifying the recent change of a data stream, specially for an online data stream, can provide valuable information for the analysis of the data stream. In addition, monitoring the continuous variation of a data stream enables to find the gradual change of embedded knowledge. However, most of mining algorithms over a data stream do not differentiate the information of recently generated transactions from the obsolete information of old transactions which may be no longer useful or possibly invalid at present. This paper proposes a data mining method for finding recent frequent itemsets adaptively over an online data stream. The effect of old transactions on the mining result of the data steam is diminished by decaying the old occurrences of each itemset as time goes by. Furthermore, several optimization techniques are devised to minimize processing time as well as main memory usage. Finally, the proposed method is analyzed by a series of experiments.

Original languageEnglish
Pages487-492
Number of pages6
DOIs
Publication statusPublished - 2003 Dec 1
Event9th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, KDD '03 - Washington, DC, United States
Duration: 2003 Aug 242003 Aug 27

Other

Other9th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, KDD '03
CountryUnited States
CityWashington, DC
Period03/8/2403/8/27

All Science Journal Classification (ASJC) codes

  • Software
  • Information Systems

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    Chang, J. H., & Lee, W. S. (2003). Finding recent frequent itemsets adaptively over online data streams. 487-492. Paper presented at 9th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, KDD '03, Washington, DC, United States. https://doi.org/10.1145/956750.956807