Performance Improvement Using Integration of Association Rule Mining and Classification Techniques

Data mining is an emerging field to find interesting patterns from a large collection of data sets. Classifcation algorithms like neural network, decision trees are used to classify the patterns according to class labels in the input patterns. Association rule mining is used for finding related or more frequent patterns found in a gived data set. This paper integrates both these techniques, namely classification and association. Experiments are carried out on different classification techniques and association techniques using Weka The main aim of this paper is to find accuracy using different classification methods and association rule mining technique. The paper proposes a new methodology of integrating both association rule mining and classification and investigated their performance and compared their results. IRIS data set from University of California, Irvine is used for evaluation purpose.