Abstract
This paper presents a hybrid data mining approach based on supervised learning and unsupervised learning to identify the closest data patterns in the data base. This technique enables to achieve the maximum accuracy rate with minimal complexity. The proposed algorithm is compared with traditional clustering and classification algorithm and it is also implemented with multidimensional datasets. The implementation results show better prediction accuracy and reliability.
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