A Sliding Window Technique for Open Data Mining over Data Streams


The KIPS Transactions:PartD, Vol. 12, No. 3, pp. 335-344, Jun. 2005
10.3745/KIPSTD.2005.12.3.335,   PDF Download:

Abstract

Recently open data mining methods focusing on a data stream that is a massive unbounded sequence of data elements continuously generated at a rapid rate are proposed actively. Knowledge embedded in a data stream is likely to be changed over time. Therefore, identifying the recent change of the knowledge quickly can provide valuable information for the analysis of the data stream. This paper proposes a sliding window technique for finding recently frequent itemsets, which is applied efficiently in open data mining. In the proposed technique, its memory usage is kept in a small space by delayed-insertion and pruning operations, and its mining result can be found in a short time since the data elements within its target range are not traversed repeatedly. Moreover, the proposed technique focused in the recent data elements, so that it can catch out the recent change of the data stream.


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Cite this article
[IEEE Style]
J. H. Chang and W. S. Lee, "A Sliding Window Technique for Open Data Mining over Data Streams," The KIPS Transactions:PartD, vol. 12, no. 3, pp. 335-344, 2005. DOI: 10.3745/KIPSTD.2005.12.3.335.

[ACM Style]
Joong Hyuk Chang and Won Suk Lee. 2005. A Sliding Window Technique for Open Data Mining over Data Streams. The KIPS Transactions:PartD, 12, 3, (2005), 335-344. DOI: 10.3745/KIPSTD.2005.12.3.335.