Missing Value Imputation of Mixed Attribute FCM Clustered Data Sets Using Higher Order Kernels

Provided by: International Journal of Emerging Technology and Advanced Engineering (IJETAE)
Topic: Data Management
Format: PDF
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.

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