Discriminative Frequent Pattern Analysis for Effective Classification

The application of frequent patterns in classification appeared in sporadic studies and achieved initial success in the classification of relational data, text documents and graphs. In this paper, we conduct a systematic exploration of frequent pattern-based classification, and provide solid reasons supporting this methodology. It was well known that feature combinations (patterns) could capture more underlying semantics than single features. However, inclusion of infrequent patterns may not significantly improve the accuracy due to their limited predictive power. By building a connection between pattern frequency and discriminative measures such as information gain and Fisher score, we develop a strategy to set minimum support in frequent pattern mining for generating useful patterns. Based on this strategy, coupled with a proposed feature selection algorithm, discriminative frequent patterns can be generated for building high quality classifiers. We demonstrate that the frequent pat...

• Type: Conference
• Year: 2007
• Where: ICDE
• Authors: Hong Cheng, Xifeng Yan, Jiawei Han, Chih-Wei Hsu
• Keywords: Database, Discriminative Frequent Patterns, Frequent Pattern Mining, Frequent Pattern-based Classification, ICDE 2007