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Exploring Data with RapidMiner by Andrew Chisholm

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Options for handling missing data

The exploration of data identifies missing data, and the overall process outside the scope of the exploration needs to consider the options for handling it and how these are affected by the type of missing data. Some guidelines are given in the following sections to help you make your decisions.

Returning to the root cause

It is obvious that missing data is a bad thing. So if it happens, it's always worth stepping back and determining why it's missing in the first place. The time spent on fixing the root cause of missing data will save time later and improve the quality of the data exploration and mining process in general.

Ignore it

Some learning algorithms cope with missing values but some do not. An example of ...

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