Presentation | 2010-06-28 Knowledge Discovery over Continuous Time-series Data Streams Wei Fan, Toyohide Watanabe, Koichi Asakura, |
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Abstract(in Japanese) | (See Japanese page) |
Abstract(in English) | Recently, a new generation of mining algorithms is necessary for real-time analysis and query response in data stream applications: for example, outlier detection from sensor network data, prediction of stock price data, web access analysis from click stream data and so on. In this paper, we propose methods for automatic trend detection over time-series data streams to discover useful knowledge. We validate the accuracy and affectivity of our methods by analyzing stock price data and news articles. According to the resultant classifiers which disclose the correlation between stock data and news articles, we can predict rise/drop trend of stock price when given a newly released news article. |
Keyword(in Japanese) | (See Japanese page) |
Keyword(in English) | Data stream mining / Trend detection / Text classification |
Paper # | DE2010-2 |
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Committee | DE |
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Conference Date | 2010/6/21(1days) |
Place (in Japanese) | (See Japanese page) |
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Registration To | Data Engineering (DE) |
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Language | ENG |
Title (in Japanese) | (See Japanese page) |
Sub Title (in Japanese) | (See Japanese page) |
Title (in English) | Knowledge Discovery over Continuous Time-series Data Streams |
Sub Title (in English) | |
Keyword(1) | Data stream mining |
Keyword(2) | Trend detection |
Keyword(3) | Text classification |
1st Author's Name | Wei Fan |
1st Author's Affiliation | Department of Systems and Social Informatics, Graduate School of Information Science, Nagoya University() |
2nd Author's Name | Toyohide Watanabe |
2nd Author's Affiliation | Department of Systems and Social Informatics, Graduate School of Information Science, Nagoya University |
3rd Author's Name | Koichi Asakura |
3rd Author's Affiliation | School of Informatics, Daido University |
Date | 2010-06-28 |
Paper # | DE2010-2 |
Volume (vol) | vol.110 |
Number (no) | 107 |
Page | pp.pp.- |
#Pages | 6 |
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