Discovering Informative Association Rule For Multi-Label Classi cation

Abstract

Data mining is the extraction of hidden predictive information from large databases. Today current work of classification is related with the single-label classification. However, many applications, such as Music, text categorization, and Medical Diagnosis, may allow the instances to be associated with multiple labels simultaneously. Multi-label classification is a generalization of single-label classification, and its generality making it much more difficult to solve compare to single -label classification. Association rule mining has also attracted wide attention in both research and application area recently. The scope and interest is increasing with modern applications its contain number of label attribute so we classify that attribute using different approaches like naive bays, decision tree, rule base classification, Backprapogation and classify by association rule based . But in all approach we used association rule analysis for multi-label datasets. We can use different data from the UCI library. Due to more no of rule generated by the multi-label association classification is more time consuming .so we try developed some techniques to reduce the redundant rule generated by the association rule mining algorithm. if it works , it show that the proposed approach is an accurate and effective classification technique, highly competitive and scalable if compared with other traditional and associative classification approaches and also overcome all problems arise in single-label classification.