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Data is real fact which undergoes various transformations under knowledge discovery. The computation in various stages of transformation requires parameters for quantifying precision, certainty and error tolerance in decision making process for end user. A multidimensional database serves the need of decision making on the basis of a knowledge discovery. This research work presents an integrated approach of knowledge discovery process and Multidimensional OLAP (MOLAP) tool. The initial step is creating a framework of computation models that formulate hybridized perceptron with fuzzification for multidimensional database management system (MDBMS) to produce crisp dataset to MOLAP. The data retrieved from multidimensional database (MDBMS), upon a structured query to MOLAP, explores the relational model for knowledge discovery process. The information retrieved thus represents a data cube for decision making. The experimental results of the proposed model have proven results for both supervised and unsupervised learning network. The approach has been adopted to serve the need of business logics and business intelligence in various knowledge discovery and hence to create an effective decision making system.