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Mining Frequent Sequential Patterns over Sequence Data Streams with a Gap-Constraint

  • Journal of The Korea Society of Computer and Information
  • Abbr : JKSCI
  • 2010, 15(9), pp.35-46
  • Publisher : The Korean Society Of Computer And Information
  • Research Area : Engineering > Computer Science

장중혁 1

1대구대학교

Accredited

ABSTRACT

Sequential pattern mining is one of the essential data mining tasks, and it is widely used to analyze data generated in various application fields such as web-based applications, E-commerce, bioinformatics, and USN environments. Recently data generated in the application fields has been taking the form of continuous data streams rather than finite stored data sets. Considering the changes in the form of data, many researches have been actively performed to efficiently find sequential patterns over data streams. However, conventional researches focus on reducing processing time and memory usage in mining sequential patterns over a target data stream, so that a research on mining more interesting and useful sequential patterns that efficiently reflect the characteristics of the data stream has been attracting no attention. This paper proposes a mining method of sequential patterns over data streams with a gap constraint, which can help to find more interesting sequential patterns over the data streams. First, meanings of the gap for a sequential pattern and gap-constrained sequential patterns are defined, and subsequently a mining method for finding gap-constrained sequential patterns over a data stream is proposed.

Citation status

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