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What is Handling Noisy Data in Data Mining about?

The document discusses the concept of noisy data, which refers to random errors or variances in measured variables due to various factors like faulty data collection and entry issues. It outlines methods for handling noisy data, including binning, regression, clustering, and human inspection, as well as techniques for treating outliers such as trimming, capping, and using z-scores. Additionally, it provides examples of identifying outliers using inter-quartile range and z-score methods.

Author

eatisamsadiq934

Language

EN