AN ITERATIVE ESTIMATOR FOR PREDICTING THE HETEROGENEOUS ATTRIBUTE DATA SETS

The quality of the patterns which are the results of data mining is depends upon the quality ofdata supplied to it. Most of the real time databases which are the sources for data mining posses thedeficiency in terms of completeness, correctness and consistency. Improving the quality of data in termsof completeness is a challenging task. Many methods were proposed for imputing the missing values forhomogenous attributes. This paper proposes a mixed kernel function, which imputes the missing valuesfor the mixed attributes (the independent attributes are heterogeneous). The mixed kernel function is anintegrated unit which adopts the right method to impute the value for right attribute. For the categoricalattribute, our kernel function first assigns the mode value and the iteration continues till the right (mostprobable) value gets converged and for the discrete attribute the mean value gets assigned and theiteration continues till the most probable value is reached. The mixed kernel function is tested with asample database; it proves that it is performing well in terms of accuracy and iterations compared tolinear kernel function.