Presentation 2005/7/25
Reconstruction of Sequential Patterns based on Dependencies
Ken Ueno, Youichi Kitahara, Toshio Hayashi, Shigeaki Sakurai, Ryohei Orihara,
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Abstract(in English) This paper proposes a pattern reconstruction method to acquire characteristic sequential patterns based on dependencies. The patterns which are joined by their dependencies can include any time lags. To compare the joined patterns with simultaneous patterns, the characteristic patterns with time lags can be extracted by c-ratio proposed in this research. With the pattern reconstruction method, we found that characteristic patterns are successfully induced from our health check database with the reconstruction method based on dependencies.
Keyword(in Japanese) (See Japanese page)
Keyword(in English) Sequential Pattern Mining / Dependency / Pattern Join / Time Series Event Pattern Mining / Health Check Data Analysis
Paper # AI2005-12
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Committee AI
Conference Date 2005/7/25(1days)
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Registration To Artificial Intelligence and Knowledge-Based Processing (AI)
Language JPN
Title (in Japanese) (See Japanese page)
Sub Title (in Japanese) (See Japanese page)
Title (in English) Reconstruction of Sequential Patterns based on Dependencies
Sub Title (in English)
Keyword(1) Sequential Pattern Mining
Keyword(2) Dependency
Keyword(3) Pattern Join
Keyword(4) Time Series Event Pattern Mining
Keyword(5) Health Check Data Analysis
1st Author's Name Ken Ueno
1st Author's Affiliation Knowledge Media Lab. Corporate Research and Development Center, Toshiba Corporation()
2nd Author's Name Youichi Kitahara
2nd Author's Affiliation Knowledge Media Lab. Corporate Research and Development Center, Toshiba Corporation
3rd Author's Name Toshio Hayashi
3rd Author's Affiliation / Knowledge Media Lab. Corporate Research and Development Center, Toshiba Corporation
4th Author's Name Shigeaki Sakurai
4th Author's Affiliation Knowledge Media Lab. Corporate Research and Development Center, Toshiba Corporation
5th Author's Name Ryohei Orihara
5th Author's Affiliation
Date 2005/7/25
Paper # AI2005-12
Volume (vol) vol.105
Number (no) 224
Page pp.pp.-
#Pages 6
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