Abstract: 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.This paper，conducts a systematic exploration of information gain based frequent pattern classification，and provides solid reasons supporting this methodology.By building a connection between pattern frequency and discriminative measures such as information gain，and also develops 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.The paper demonstrates that the information gain based frequent pattern classification framework can achieve good scalability and high accuracy in classifying large datasets.