Presentation 2007-05-31
An Analysis of Sequential Data with Hierarchical Structure of Time Frame Using Frequent Subtree Mining
Ryohei FUKUDA, Hiroyuki OONO, Hiroshige INAZUMI,
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Abstract(in English) Finding hierarchical relations from categorical time series data can be an effective way for feature extraction. To do this, we convert the data into the ordered tree structures, and get sub-patterns with wild cards using a tree mining algorithm. As a result, we usually get many sub-patterns. So, we will use clusters of sub-patterns made by these supports of the time series data, and find characteristics of the data. We will use this method for classification problem.
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Keyword(in English) Data Mining / Time Series Data / Graph Mining / Semi Structured Data
Paper # AI2007-9
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Committee AI
Conference Date 2007/5/24(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) An Analysis of Sequential Data with Hierarchical Structure of Time Frame Using Frequent Subtree Mining
Sub Title (in English)
Keyword(1) Data Mining
Keyword(2) Time Series Data
Keyword(3) Graph Mining
Keyword(4) Semi Structured Data
1st Author's Name Ryohei FUKUDA
1st Author's Affiliation Graduate school of Science and Engineering, Aoyama Gakuin University()
2nd Author's Name Hiroyuki OONO
2nd Author's Affiliation College of Science and Engineering, Aoyama Gakuin University
3rd Author's Name Hiroshige INAZUMI
3rd Author's Affiliation College of Science and Engineering, Aoyama Gakuin University
Date 2007-05-31
Paper # AI2007-9
Volume (vol) vol.107
Number (no) 78
Page pp.pp.-
#Pages 6
Date of Issue