Presentation | 1999/1/23 Parallel SQL Based Association Rule Mining on Large Scale PC Cluster : Performance Comparison with Directly Coded C Implementation Iko Pramudiono, Takahiko Shintani, Takayuki Tamura, Masaru Kitsuregawa, |
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Abstract(in English) | Data mining is becoming increasingly important since the size of databases grows even larger and the need to explore hidden rules from the databases become widely recognized.Currently database systems dominated by relational database and the ability to perform data mining using standard SQL query will definitely ease implementation of data mining.However SQL based data mining performance is known to fall behind specialized implementation.In this paper we presented an evaluation of data mining based on parallel SQL on large scale PC cluster. For flat association rule mining, parallelizing SQL query using 4 processing nodes the performance is even with C based program while in case of generalized association rule with taxonomy, we achieve comparable result with 6-8 processing nodes. |
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Paper # | DE98-56 |
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Committee | DE |
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Conference Date | 1999/1/23(1days) |
Place (in Japanese) | (See Japanese page) |
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Registration To | Data Engineering (DE) |
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Language | JPN |
Title (in Japanese) | (See Japanese page) |
Sub Title (in Japanese) | (See Japanese page) |
Title (in English) | Parallel SQL Based Association Rule Mining on Large Scale PC Cluster : Performance Comparison with Directly Coded C Implementation |
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1st Author's Name | Iko Pramudiono |
1st Author's Affiliation | Institute of Industrial Science, The University of Tokyo() |
2nd Author's Name | Takahiko Shintani |
2nd Author's Affiliation | Institute of Industrial Science, The University of Tokyo |
3rd Author's Name | Takayuki Tamura |
3rd Author's Affiliation | Institute of Industrial Science, The University of Tokyo |
4th Author's Name | Masaru Kitsuregawa |
4th Author's Affiliation | Institute of Industrial Science, The University of Tokyo |
Date | 1999/1/23 |
Paper # | DE98-56 |
Volume (vol) | vol.98 |
Number (no) | 561 |
Page | pp.pp.- |
#Pages | 8 |
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