SMS Classification Based on Naïve Bayes Classifier and Apriori Algorithm Frequent Itemset

In this paper, we propose a hybrid system of SMS classification to detect spam or ham, using Na ve Bayes classifier and Apriori algorithm. Though this technique is fully logic based, its performance will rely on statistical character of the database. Na ve Bayes is considered as one of the most effectual and significant learning algorithms for machine learning and data mining and also has been treated as a core technique in information retrieval. However, by applying user-specified minimum support and minimum confidence, we gain significant improvement on effective accuracy 98.7% from the traditional Na ve Bayes approach 97.4% experimenting on UCI Data Repository.