| Presentation | 2005-06-16 Self-Similarity based Clustering of time series Aki ITO, Osamu KONISHI, |
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| Abstract(in Japanese) | (See Japanese page) |
| Abstract(in English) | Large time series data mining is an advanced research direction in the database world. An adaptive clustering method for very large time series with a self-similarity is taken as the objective of research in this paper. Firstly, based on fractal theory, the fractal aspect of the time series is analyzed in keeping with the space domain that consists of the time axis and the amplitude axis. Their measured fractal dimensions are applied to the clustering algorithm as input vector. Then, new data added incrementally can be classified effectively by their fractal dimensions. At last, we show that the technique could be used for the analysis of EEG. |
| Keyword(in Japanese) | (See Japanese page) |
| Keyword(in English) | Time series / Clustering / Self-Similarity / Fractal dimension / EEG |
| Paper # | DE2005-5,PRMU2005-26 |
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| Conference Information | |
| Committee | PRMU |
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| Conference Date | 2005/6/9(1days) |
| Place (in Japanese) | (See Japanese page) |
| Place (in English) | |
| Topics (in Japanese) | (See Japanese page) |
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| Registration To | Pattern Recognition and Media Understanding (PRMU) |
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| Language | JPN |
| Title (in Japanese) | (See Japanese page) |
| Sub Title (in Japanese) | (See Japanese page) |
| Title (in English) | Self-Similarity based Clustering of time series |
| Sub Title (in English) | |
| Keyword(1) | Time series |
| Keyword(2) | Clustering |
| Keyword(3) | Self-Similarity |
| Keyword(4) | Fractal dimension |
| Keyword(5) | EEG |
| 1st Author's Name | Aki ITO |
| 1st Author's Affiliation | Future University-Hakodata, Graduate School() |
| 2nd Author's Name | Osamu KONISHI |
| 2nd Author's Affiliation | Future University-Hakodata |
| Date | 2005-06-16 |
| Paper # | DE2005-5,PRMU2005-26 |
| Volume (vol) | vol.105 |
| Number (no) | 118 |
| Page | pp.pp.- |
| #Pages | 6 |
| Date of Issue | |