%0 Journal Article %1 saravanan2014iterative %A Saravanan, P. %D 2014 %E kr. Thakur, Ajay %J Weekly Science Research Journal %K Attributes Data Heterogeneous Missing imputation. mining value %N 27 %T An Iterative Estimator for Predicting the Heterogeneous Attribute Data Sets %U http://weeklyscience.org/UploadedArticle/64.pdf %V 1 %X 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.

@article{saravanan2014iterative, 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. }, added-at = {2014-08-16T11:53:38.000+0200}, author = {Saravanan, P.}, biburl = {https://www.bibsonomy.org/bibtex/287995dc65d28cb231273046863065c30/sciencejournal}, editor = {kr. Thakur, Ajay}, interhash = {faaee6da944aa9391483a9bad9c758bb}, intrahash = {87995dc65d28cb231273046863065c30}, journal = { Weekly Science Research Journal}, keywords = {Attributes Data Heterogeneous Missing imputation. mining value}, month = {January}, number = 27, timestamp = {2014-08-16T11:53:38.000+0200}, title = {An Iterative Estimator for Predicting the Heterogeneous Attribute Data Sets }, url = {http://weeklyscience.org/UploadedArticle/64.pdf}, volume = 1, year = 2014 }