Building an Iris Plant Data Classifier Using Neural Network Associative Classification

Classification rule mining is used to discover a small set of rules in the database to form an accurate classifier. Association rules mining are used to reveal all the interesting relationship in a potentially large database. For association rule mining, the target of the discovery is not predetermined, while for classification rule mining there is one and only one predetermined target. These two techniques can be integrated to form a framework called Associative Classification method. The integration is done by focusing on mining a special subset of association rules called Class Association rules (CAR).This project paper proposes a Neural Network Association Classification system, which is one of the approaches for building accurate and efficient classifiers. Experimental result shows that the classifier build for iris plant dataset in this way is more accurate than previous Classification Based Association.