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...

• Type: Conference
• Year: 2008
• Where: ICDE
• Authors: Hong Cheng, Xifeng Yan, Jiawei Han, Philip S. Yu
• Keywords: Database, Discriminative Pattern Mining, Discriminative Patterns, Frequent Pattern Mining, ICDE 2008