Discretization algorithms: Class-Attribute Contingency Coefficient

Version 1.2.0.0 (1,8 Mo) par Guangdi Li
To discrete continuous data, CACC is a promising discretization scheme proposed in 2008
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Mise à jour 31 jan. 2011

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Discretization algorithms have played an important role in data mining and knowledge discovery. They not only produce a concise summarization of continuous attributes to help the experts understand the data more easily, but also make learning more accurate and faster.
We implement the CACC algorithm is based on paper[1].
As for the code, one can open "ControlCenter.m" at first, there is a simple example here, along with one yeast database. Explanation is included inside this file too.
If there is any problem, just let me know, i will help you as soon as possible.

[1]Cheng-Jung Tsai, Chien-I Lee, Wei-Pang Yang: A discretization algorithm based on Class-Attribute Contingency Coefficient. Inf. Sci. 178(3): 714-731 (2008)

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Guangdi Li (2024). Discretization algorithms: Class-Attribute Contingency Coefficient (https://www.mathworks.com/matlabcentral/fileexchange/24343-discretization-algorithms-class-attribute-contingency-coefficient), MATLAB Central File Exchange. Récupéré le .

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