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 of<br>data supplied to it. Most of the real time databases which are the sources for data mining posses the<br>deficiency in terms of completeness, correctness and consistency. Improving the quality of data in terms<br>of completeness is a challenging task. Many methods were proposed for imputing the missing values for<br>homogenous attributes. This paper proposes a mixed kernel function, which imputes the missing values<br>for the mixed attributes (the independent attributes are heterogeneous). The mixed kernel function is an<br>integrated unit which adopts the right method to impute the value for right attribute. For the categorical<br>attribute, our kernel function first assigns the mode value and the iteration continues till the right (most<br>probable) value gets converged and for the discrete attribute the mean value gets assigned and the<br>iteration continues till the most probable value is reached. The mixed kernel function is tested with a<br>sample database; it proves that it is performing well in terms of accuracy and iterations compared to<br>linear kernel function.