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CAIM Discretization Algorithm

version (1.8 MB) by Guangdi Li
CAIM (class-attribute interdependence maximization) is designed to discretize continuous data.


Updated 17 Oct 2010

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The task of extracting knowledge from databases is quite often performed by machine learning algorithms. The majority of
these algorithms can be applied only to data described by discrete numerical or nominal attributes (features). In the case of continuous attributes, there is a need for a discretization algorithm that transforms continuous attributes into discrete ones.
This code is based on paper: "CAIM Discretization Algorithm", details can look for this paper [1].
One can start with "ControlCenter.m", here you will find a simple example , with explanations.
If there is any problem , please let me know. I will answer you as soon as possible.

[1] Kurgan, L. and Cios, K.J., 2004. CAIM Discretization Algorithm. IEEE Transactions on Knowledge and Data Engineering, 16(2):145-153

Comments and Ratings (5)


How can I increase the number of discretized classes that the algorithm outputs. Right now I can only get 3 discretized classes.

Hello sir i am student of jntuk university..
can u help me example of CAIM discretization algorithm then i understod code please send me example.......
i am waiting for reply its urgent.....


Thanks for the code Guangdi Li.
I have a question regarding the class labels. I am not able to understand the class labels assigned to the Yeast dataset. Aren't the class label supposed to be a binary indicator matrix with 1ofK coding?

Guangdi Li

Sorry, I am not familiar with Statlog (Heart) Data Set (270, 13)? Could you please send me the data directly? Then I could test it and find the problem. Thanks

Yu Li

Hi, I got a error, can u help me?
Attempted to access B(0); index must be a positive integer or logical.
Error in ==> CAIM_Discretization at 73
D( k ) = B( Local );
I use Statlog (Heart) Data Set (270, 13).


As requested by the authors of CAIM, I add their paper in reference.

MATLAB Release Compatibility
Created with R2008a
Compatible with any release
Platform Compatibility
Windows macOS Linux