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 Japanese) | (See Japanese page) |
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. |
Keyword(in Japanese) | (See Japanese page) |
Keyword(in English) | Data Mining / Time Series Data / Graph Mining / Semi Structured Data |
Paper # | AI2007-9 |
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Committee | AI |
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Conference Date | 2007/5/24(1days) |
Place (in Japanese) | (See Japanese page) |
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Registration To | Artificial Intelligence and Knowledge-Based Processing (AI) |
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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 |
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