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K2 algorithm for learning DAG structure in Bayesian network

12 Mar 2009 (Updated 05 Aug 2009)

Code covered by the BSD License

This is the code of Cooper's K2 algorithm proposed in 1992, quick and convenient for using.

File Information
Description	K2 algorithm is the most famous score-based algorithm in Bayesian netowrk in the last two decades. Specifically, it recovers the underlying distribution in the form of DAG efficiently. For details, please refer to Cooper's published paper[1] Please start from "ControlCentor.m", here is a simple example for understanding how to use our code. If there is any question, please let me know, i will help you as soon as possible. I use rewritten the K2 by mex programming, if you know how to compile it, please try K2.c because this code is able to handle the variables as large as 1000 variables, much efficient. It's tested under linux both 32-bit and 64-bit. [1] G. Cooper and E. Herskovitz, A Bayesian method for the induction of probabilistic networks from data, Machine Learning 9 (1992), 330–347.
MATLAB release	MATLAB 7.6 (R2008a)
Zip File Content
Other Files	K2/ControlCentor.m, K2/K2.c, K2/k2.m, K2/Sample.mat, K2/subfunction/ConstructLGObj.m, K2/subfunction/GClosedFun.m, license.txt

Tags for This File
Everyone's Tags	bayesian network, dag, data mining
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Comments and Ratings (1)
28 Mar 2009	Lowell Guangdi	There is one file named 'DimensionRangeValue.m' is missing, I am sorry for any inconvenience of you working. The updated version will be published in next week (March 20) after Matlab Center checks it. Enjoying you using my codes.

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Updates
28 Mar 2009	One file is missing, sorry, I add it again.
17 Jun 2009	I improve the efficiency of this algorithm again.
27 Jun 2009	Update the code to output the K2 score upon each node
05 Aug 2009	improve the efficiency of code by mex programming.

Tag Activity for this File
Tag	Applied By	Date/Time
dag	Lowell Guangdi	13 Mar 2009 13:22:05
bayesian network	Lowell Guangdi	13 Mar 2009 13:22:06
data mining	Lowell Guangdi	13 Mar 2009 13:22:06

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