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| Presentation | 2007-07-03 15:25 Parallel Frequent Pattern Mining Method from Super High-Dimensional Data by Vertical Partitioning Kouichirou Mori, Ryohei Orihara (Toshiba Corp.)  DE2007-91 |
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| Abstract | (in Japanese) | (See Japanese page) |
| (in English) | In general, traditional parallel frequent pattern mining methods were applied to data that contains a large number of records. The data was horizontally partitioned and each partitioned data was allocated to processing elements. However recently, frequent pattern mining from super-high-dimensional data that contains a huge number of attributes is becoming important. The traditional parallel frequent pattern mining methods cannot handle these data. In this paper, we show that the combination of vertical partitioning and record space search is efficient for parallel frequent pattern mining of high-dimensional data. We evaluate our method with real microarray dataset on 16 PCs to discover that it is approximately 13 times faster than sequential one. | |
| Keyword | (in Japanese) | (See Japanese page) |
| (in English) | Data Mining / Association Rule / Frequent Pattern / High Dimensional Data / Vertical Partitioning / Parallel Processing / / | |
| Reference Info. | IEICE Tech. Rep., vol. 107, no. 131, DE2007-91, pp. 417-422, July 2007. | |
| Paper # | DE2007-91 | |
| Date of Issue | 2007-06-25 (DE) | |
| ISSN | Print edition: ISSN 0913-5685 Online edition: ISSN 2432-6380 | |
| Copyright and reproduction |
All rights are reserved and no part of this publication may be reproduced or transmitted in any form or by any means, electronic or mechanical, including photocopy, recording, or any information storage and retrieval system, without permission in writing from the publisher. Notwithstanding, instructors are permitted to photocopy isolated articles for noncommercial classroom use without fee. (License No.: 10GA0019/12GB0052/13GB0056/17GB0034/18GB0034) | |
| Download PDF | DE2007-91 |
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| Conference Information | |
| Committee | DE |
| Conference Date | 2007-07-02 - 2007-07-04 |
| Place (in Japanese) | (See Japanese page) |
| Place (in English) | Akiu hot springs (Sendai) |
| Topics (in Japanese) | (See Japanese page) |
| Topics (in English) | Summer Database Workshop 2007 (Data engineering, etc.) |
| Paper Information | |
| Registration To | DE |
| Conference Code | 2007-07-DE |
| Language | Japanese |
| Title (in Japanese) | (See Japanese page) |
| Sub Title (in Japanese) | (See Japanese page) |
| Title (in English) | Parallel Frequent Pattern Mining Method from Super High-Dimensional Data by Vertical Partitioning |
| Sub Title (in English) | |
| Keyword(1) | Data Mining |
| Keyword(2) | Association Rule |
| Keyword(3) | Frequent Pattern |
| Keyword(4) | High Dimensional Data |
| Keyword(5) | Vertical Partitioning |
| Keyword(6) | Parallel Processing |
| Keyword(7) | |
| Keyword(8) | |
| 1st Author's Name | Kouichirou Mori |
| 1st Author's Affiliation | Toshiba Corporation (Toshiba Corp.) |
| 2nd Author's Name | Ryohei Orihara |
| 2nd Author's Affiliation | Toshiba Corporation (Toshiba Corp.) |
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| Speaker | Author-1 |
| Date Time | 2007-07-03 15:25:00 |
| Presentation Time | 25 minutes |
| Registration for | DE |
| Paper # | DE2007-91 |
| Volume (vol) | vol.107 |
| Number (no) | no.131 |
| Page | pp.417-422 |
| #Pages | 6 |
| Date of Issue | 2007-06-25 (DE) |
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