Hot Encode vs Binary Encoding for Binary attribute when clustering

Question

I am planning to use data for a clustering problem that contains a column with a binary value BUY/SELL.

Should I be converting this attribute and assign it binary values (BUY=1, SELL=0), and keep it on the same column, thus reducing the number dimensions

OR

Hot encode the attribute (adding two columns BUY and SELL and putting 1 on the appropriate column)?

How do these two methods of nominal to numeric conversion affect the final model for popular clustering algorithms (K means, Hierarchical, etc...)

Kasra Manshaei · Answer

Not much of difference in your case. The difference is in just 1 dimension which does not affect much. The only point I can add is that if the number of BUY and SELL values are not the same, you can replace them with their frequencies i.e. if 40% BUY and 60% SELL, then replace BUY with 0.4 and SELL with 0.6

Answered by Kasra Manshaei on September 26, 2021

Venkat · Answer

If one value has more priority than the other, then you can go with binary encoding. e.x) If the values are based on education level, you can assign 0 to school-level education and 1 to college-level education.
If the values do not have any arithmetical dependency, then you need to go for one-hot encoding.
In your case, hot encoding is better.
Edit: If we have only two values, either binary encoding or hot encoding will work. This edit is based on the comment from @beamsadept.

Hot Encode vs Binary Encoding for Binary attribute when clustering

2 Answers

Add your own answers!

Ask a Question