Standardization vs. normalization
In the overall knowledge discovery process, before data mining itself, data preprocessing plays a crucial role. One of the first steps concerns the normalization of the data. This step is very important when dealing with parameters of different units and scales. For example, some data mining techniques use the Euclidean distance. Therefore, all parameters should have the same scale for a fair comparison between them.
Two methods are usually well known for rescaling data. Normalization, which scales all numeric variables in the range [0,1]. One possible formula is given below:
On the other hand, you can use standardization on your data set. It will then transform it to have zero mean and unit variance, for example using the equation below:
Both of these techniques have their drawbacks. If you have outliers in your data set, normalizing your data will certainly scale the "normal" data to a very small interval. And generally, most of data sets have outliers. When using standardization, you make an assumption that your data have been generated with a Gaussian law (with a certain mean and standard deviation). This may not be the case in reality.
So my question is what do you usually use when mining your data and why?
Note: Thanks to Benny Raphael for fruitful discussions on this topic.
