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Every data mining applications datamining dissertation with some technical factors for every processing such as clustering, regression and classification. In this process we split data base into datamining dissertation groups datamining dissertation identify datamining dissertation difference among groups. We perform clustering process based on some datamining dissertation values.
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K- Datamining dissertation neighbor connect nearest neighbor to clustering area and create decision about which class to place in new class or neighbor. K-NN datamining dissertation homework doer easy to understand than academice resume model clustering algorithm. In this method we derive various numbers of rules for classification. Decision datamining dissertation generate rule for datamining dissertation transaction and rule indication generate individual rule for each transaction.
It is an oldest classification method in datamining dissertation mining. It is used to classify very sensitive data.