Spam Short Message Classifier Model Based on Association Rules
Zhang Yong-ju · Journal of Nantong University · 2014
A classifier model based on association rules was proposed to filter spam short messages(SSM). To mine association rules which were used to build the spam classifier model, an improved FP-grow algorithm was put forward. The factors of word weights and spam variation behaviors were considered to make SSM classifying more effective. The experimental results show that the proposed model is superior to other classification methods in the aspect of precision and misclassification rate of non-SSM.