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2008
IEEE

Direct Discriminative Pattern Mining for Effective Classification

12 years 2 months ago
Direct Discriminative Pattern Mining for Effective Classification
The application of frequent patterns in classification has demonstrated its power in recent studies. It often adopts a two-step approach: frequent pattern (or classification rule) mining followed by feature selection (or rule ranking). However, this two-step process could be computationally expensive, especially when the problem scale is large or the minimum support is low. It was observed that frequent pattern mining usually produces a huge number of "patterns" that could not only slow down the mining process but also make feature selection hard to complete. In this paper, we propose a direct discriminative pattern mining approach, DDPMine, to tackle the efficiency issue arising from the two-step approach. DDPMine performs a branch-andbound search for directly mining discriminative patterns without generating the complete pattern set. Instead of selecting best patterns in a batch, we introduce a "feature-centered" mining approach that generates discriminative patte...
Hong Cheng, Xifeng Yan, Jiawei Han, Philip S. Yu
Added 01 Nov 2009
Updated 01 Nov 2009
Type Conference
Year 2008
Where ICDE
Authors Hong Cheng, Xifeng Yan, Jiawei Han, Philip S. Yu
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