Hybrid Information Systems
Application Mining
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Real world databases dealing with quantitative variables require subjective and intelligent analysis for exhaustive knowledge discovery through which decision making becomes more efficient. To achieve this, the work proposes a hybrid information system. The proposed system fuses Rule Induction, Fuzzy transformations, and Artificial Neural Network (ANN). Data pre processing, deeper data search for knowledge discovery, and wider predictive decision making are the process functions handled by the system design.The proposed Adaptive Fuzzy Apriori (AFA) Tree search algorithm for knowledge discovery...
Real world databases dealing with quantitative variables require subjective and intelligent analysis for exhaustive knowledge discovery through which decision making becomes more efficient. To achieve this, the work proposes a hybrid information system. The proposed system fuses Rule Induction, Fuzzy transformations, and Artificial Neural Network (ANN). Data pre processing, deeper data search for knowledge discovery, and wider predictive decision making are the process functions handled by the system design.The proposed Adaptive Fuzzy Apriori (AFA) Tree search algorithm for knowledge discovery and the Rule Based algorithm for decision making run on the system enhance the performance of the system to be highly intelligent and compatible.
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