Mining of Optimized Multi Relational Relation Patterns for Prediction System

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Prediction is arguably considered the main goal of data mining, with the greatest potential payoff. The two principal prediction problems are classification and regression. In many data mining tools that support classification tasks, training data are stored in a single table containing both the target field and the attributes. Generally, only intra-tuple relationships between the attributes and the target field are found, while inter-tuple relationships are not considered and relationships between several tuples of distinct tables are not even able explore. Disregarding inter-table relationships can be a severe limitation in many real-world applications that involve the prediction of numerical values from data that are naturally organized in a relational model involving several tables. In this paper, we have concentrated on classification task of multi-relational data. We have planned to develop an optimized rule generation with the newly designed fitness function of genetic algorithm. Once the rule is mined, the relevant attributes will be identified from multi relational patterns. Then, the database is converted to single table with all the relevant attributes from multiple tables. Finally, the neural network will be used to training the converted data based on the association relationship. Then, in the testing phase, the prediction will be done with the help of trained neural network. In the experimental analysis, the proposed approach has gained an average accuracy of 95% in the prediction process by neural network.
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Prediction; Multi-Relational Data; Pattern Mining; Optimization; Artificial Neural Network

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