  Missing attribute values are quite common in the datasets available in the
literature. Missing values are also possible because all attributes values may
not be recorded and hence unavailable due to several practical reasons. For all
these one must fix missing attribute vales if the analysis has to be done.
Imputation is the first step in analyzing medical datasets. Hence this has
achieved significant contribution from several medical domain researchers.
Several data mining researchers have proposed various methods and approaches to
impute missing values. However very few of them concentrate on dimensionality
reduction. In this paper, we discuss a novel imputation framework for missing
values imputation. Our approach of filling missing values is rooted on class
based clustering approach and essentially aims at medical records
dimensionality reduction. We use these dimensionality records for carrying
prediction and classification analysis. A case study is discussed which shows
how imputation is performed using proposed method.
