Classification and association rule mining are two basic tasks of Data Mining. Classification rule mining is used to discover a small set of rules in the database to form an accurate classifier. Association rules mining has been used to reveal all interesting relationships in a potentially large database. An Apriori approach, which was used to generate the association rules from frequent patterns, turn out to generate a huge time-intensive query called as iceberg query. Various researches have been done under the Apriori-like approach to improve performance of the frequent pattern mining tasks but the results were not as much as expected due to many scans on the dataset. This project aims to propose a flexible way of mining frequent patterns by extending the idea of the Associative Classification methods. For better performance, the Neural Network Association Classification system is proposed here to be one of the approaches for building accurate and efficient classifiers. In this paper, the Neural Network Association Classification system is used in order to improve its accuracy. The structure of the network reflects the knowledge uncovered in the previous discovery phase. The trained network is then used to classify unseen data. The performance of the Neural Network Associative Classification system is compared with the previous Classification Based Association on four datasets from UCI machine learning repository.

Data mining Class Association Rules classification Backpropagation neural network