Log transformation

We should use this data transformation when an attribute experiences exponential growth and decline across the population of our data objects. When you draw a box plot of these attributes, you expect to see fliers, but those are not mistaken records, nor are they unnatural outliers. Those significantly larger or smaller values come naturally from the environment.

Attributes with exponential growth or decline may be problematic for data visualization and clustering analysis; furthermore, they can be problematic for some prediction and classification algorithms where the method uses the distance between the data objects, such as KNN, or where the method drives its performance based on collective performance metrics, such as linear regression.

These attributes may sound very hard to deal with, but there is a very easy fix for them – log transformation. In short, instead of using the attribute, you calculate the logarithms of all of the values and use them...