A Survey on Approaches of Multirelational Classification Based on Relational Database
Shraddha Modi1, Amit Thakkar2, Amit Ganatra3
2Amit Thakkar, Department of Information Technology, Chandubhai S Patel Institute of Technolog, Changa, India.
3Amit Ganatra, U and P.U. Patel Department of Computer Engineering, Chandubhai S Patel Institute of Technology, Changa, India.
1Shraddha Modi, U and P.U. Patel Department of Computer Engineering, Chandubhai S Patel Institute of Technology, Changa, India.Manuscript received on january 17, 2012. | Revised Manuscript received on February 05, 2012. | Manuscript published on February 29, 2012. | PP:77-81 | Volume-1 Issue-3, February 2012. | Retrieval Number: C0198021312/2011©BEIESP
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Abstract: Classification is an important task in data mining and machine learning, in which a model is generated based on training dataset and that model is used to predict class label of unknown dataset. Today most real-world data are stored in relational databases. So to classify objects in one relation, other relations provide crucial information. Relational databases are the popular format for structured data which consist of tables connected via relations (primary key/ foreign key). So relational databases are simply too complex to analyse with a propositional algorithm of data mining. To classify data from relational database need of multi relational classification arise which is used to analyze relational database and used to predict behaviour and unknown pattern automatically which include credit card fraud detection, disease diagnosis system, financial decision making system, information extraction and face recognition applications. This paper presents survey of different approaches to classify data from multiple relations, which includes Flattening based approach, Upgrading approach and Multiple view based approach.
Keywords: Inductive logic programming, Multi relational classification, Multiple view, Multi-view, Relational database, Selection graph, Tuple id propagation.