Babu, TR and Murty, MN and Agrawal, VK (2005) Hybrid learning scheme for data mining applications. In: Hybrid learning scheme for data mining applications, DEC 05-08, 2004, Kitakyushu.
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Classification of large datasets is a challenging task in Data Mining. In the current work, we propose a novel method that compresses the data and classifies the test data directly in its compressed form. The work forms a hybrid learning approach integrating the activities of data abstraction, frequent item generation, compression, classification and use of rough sets.
|Item Type:||Conference Paper|
|Additional Information:||Copyright 2005 IEEE. Personal use of this material is permitted. However, permission to reprint/republish this material for advertising or promotional purposes or for creating new collective works for resale or redistribution to servers or lists, or to reuse any copyrighted component of this work in other works must be obtained from the IEEE.|
|Department/Centre:||Division of Electrical Sciences > Computer Science & Automation (Formerly, School of Automation)|
|Date Deposited:||27 Apr 2010 12:11|
|Last Modified:||19 Sep 2010 06:00|
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Hybrid learning scheme for data mining applications. (deposited 08 Jun 2010 05:04)
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