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Fifth IEEE International Conference on Data Mining (ICDM'05)
Extracting Frequent Subsequences from a Single Long Data Sequence: A Novel Anti-Monotonic Measure and a Simple On-Line Algorithm
Houston, Texas
November 27-November 30
ISBN: 0-7695-2278-5
Koji Iwanuma, University of Yamanashi
Ryuichi Ishihara, University of Yamanashi
Yo Takano, University of Yamanashi
Hidetomo Nabeshima, University of Yamanashi
In this paper, we study frequent-subsequence extraction from a single very-long data-sequence. First we propose a novel frequency measure, called the total frequency, for counting multiple occurrences of a sequential pattern in a single data sequence. The total frequency is anti-monotonic, and makes it possible to count up pattern occurrences without duplication. Moreover the total frequency has a good property for implementation based on the dynamic programming strategy. Second we give a simple on-line algorithm for a specialized subsequence extraction problem, i.e., a problem with the infinite window-length. This specialized problem is considered to be a relaxation of the general-case problem, thus this fast on-line algorithm is important from the view of practical applications.
Citation:
Koji Iwanuma, Ryuichi Ishihara, Yo Takano, Hidetomo Nabeshima, "Extracting Frequent Subsequences from a Single Long Data Sequence: A Novel Anti-Monotonic Measure and a Simple On-Line Algorithm," icdm, pp.186-193, Fifth IEEE International Conference on Data Mining (ICDM'05), 2005
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