Link-Based Classification for MultiRelational Database

Classification is most popular data mining tasks with a wide range of applications. As converting data from multiple relations into single flat relation usually causes many problems so classification task across multiple database relations becomes challenging task. It is counterproductive to convert multi-relational data into single flat table because such conversion may lead to the generation of huge relation and lose of essential semantic information. In this paper we propose two algorithms for Multi-Relational Classification (MRC). To take advantage of linkage relationship and to link target table with different tables, a semantic relationship graph (SRG) is used. In First approach we have used Naive Bayesian Combination to combine heterogeneous classifiers result to get class label. This will classify the instance accurately and efficiently. Second approach is Multi-Relational Classification using Decision Template (DT). Decision profile is created to combine heterogeneous classifiers output. Based on similarity measure decision template and decision profile is compared to get final output. DT takes contribution of each classifiers output rather than class-conscious. So classification accuracy is improved.