Missing Value Imputation of Mixed Attribute FCM Clustered Data Sets Using Higher Order Kernels
Data mining is the efficient discovery of previously unknown, valid, potentially useful, understandable patterns in large datasets. There is a need for quality of data, thus the quality of data is ultimately important. The existing system provides a new setting for missing data imputation, i.e., imputing missing data in data sets with heterogeneous attributes (their independent attributes are of different types), referred to as imputing mixed-attribute data sets. The system provides various estimators to impute the missing data. A mixture-kernel based iterative estimator is advocated to impute mixed-attribute data sets. The proposed system implements the estimators with higher order kernels such as Spherical kernel and Bayesian kernel and a new setting of handling missing data imputation in FCM classified data sets.