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| | Articles of Volume : 1 Issue : 27 ( 23, January - 2014) | | | | 64 | An Iterative Estimator for Predicting the Heterogeneous Attribute Data Sets | | By : P. Saravanan | | Abstract : The quality of the patterns which are the results of data mining is depends
upon the quality of data supplied to it. Most of the real time databases which
are the sources for data mining possess the deficiency in terms of
completeness, correctness and consistency. Improving the quality of data in
terms of completeness is a challenging task. Many methods were proposed for
imputing the missing values for homogenous attributes. This paper proposes a
mixed kernel function, which imputes the missing values for the mixed
attributes (the independent attributes are heterogeneous). The mixed kernel
function is an integrated unit which adopts the right method to impute the
value for right attribute. For the categorical attribute, our kernel function first
assigns the mode value and the iteration continues till the right (most
probable) value gets converged and for the discrete attribute the mean value
gets assigned and the iteration continues till the most probable value is
reached. The mixed kernel function is tested with a sample database; it proves
that it is performing well in terms of accuracy and iterations compared to linear
kernel function. | | | | Author Profile | Full-Text PDF | |
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