Publication | Closed Access
Extracting Frequent Subsequences from a Single Long Data Sequence: A Novel Anti-Monotonic Measure and a Simple On-Line Algorithm
17
Citations
9
References
2006
Year
Unknown Venue
EngineeringPattern DiscoveryPattern MiningNovel Frequency MeasureData ScienceData MiningStatisticsSimple On-line AlgorithmKnowledge DiscoveryComputer SciencePattern MatchingNovel Anti-monotonic MeasureFrequent Pattern MiningData Stream MiningCombinatorial Pattern MatchingTotal FrequencyFrequent Subsequence ExtractionStructure MiningFrequent SubsequencesBig Data
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.
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