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Abstract

This disclosure relates generally to data preprocessing, and more particularly to implementing data pre-processing through outlier analysis and multivariate imputation process. In one embodiment, the method includes performing iterations for processing integrated data associated with a manufacturing process. Each iteration comprises removing outliers from the integrated data using a multi-level outlier model to obtain a filtered data. The filtered data is categorized into multiple categories to identify missing data based on a frequency of occurrence of various parameters. Missing data is selectively imputed based on the multiple categories to obtain imputed data which is clustered into various data clusters based on a predefined criteria. After every iteration, it is determined whether the imputed data associated with a current iteration is clustered into the same data clusters as associated with a previous iteration. Various iterations are performed until the data clusters in the previous iteration and the current iterations are similar.

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First Claim