近日来看了一些决策树的资料，在网上查找资料时，深感决策树的资料不是很多，特将本人能用上的一些资料共享在此。在此感谢数据挖掘研究院的论坛，里面有许多这方面的资料，只是没有整理在一起。
C5.0算法的Demo程序
http://www.rulequest.com/download.html
http://www.rulequest.com/See5-demo.zip

C5.0算法说明
See5: An Informal Tutorial
http://www.rulequest.com/see5-win.html

id3 和 c4.5代码公共包
http://218.22.25.142:8080/upload/92.zip
现在已经修改为 http://www.dmresearch.net/forum/upload/92.zip

c5.0算法源代码 (c语言版)
http://218.22.25.142:8080/upload/120.zip

决策树算法及应用拓展
http://218.22.25.142:8080/upload/204.zip

http://www2.cs.uregina.ca/~hamilton/courses/831/index.html
http://www2.cs.uregina.ca/~hamilton/courses/831/notes/ml/dtrees/4_dtrees1.html
详细讲述C4.5算法的步骤  

Build classification tree in Excel using C4.5  
http://www.geocities.com/adotsaha/CTree/CtreeinExcel.html

决策树算法ID3和C4.5的提出者Ross Quinlan的个人网页
http://www.cse.unsw.edu.au/~quinlan/

Sample Applications Using See5/C5.0
Predicting Magnetic Properties of Crystals
Profiling High Income Earners from Census Data
Assessing Churn Risk
Detecting Advertisements on the Web
Identifying Spam
Diagnosing Hypothyroidism
Now Read On ...
http://www.rulequest.com/see5-examples.html

Free Decision Tree Software for Classification
free and shareware:

C4.5, the "classic" decision-tree tool, developed by J. R. Quinlan, (restricted distribution)
http://www.cse.unsw.edu.au/~quinlan/

Classification Tree in Excel, from Angshuman Saha
http://www.geocities.com/adotsaha/CTree/CtreeinExcel.html

IND, provides Gini and C4.5 style decision trees and more. Publicly available from NASA but with export restrictions.
http://ic.arc.nasa.gov/projects/bayes-group/ind/IND-program.html

LMDT, builds Linear Machine Decision Trees (based on Brodley and Utgoff papers).
http://mow.ecn.purdue.edu/~brodley/software/lmdt.html

OC1, decision tree system continuous feature values; builds decision trees with linear combinations of attributes at each internal node; these trees then partition the space of examples with both oblique and axis-parallel hyperplanes.
http://www.cs.jhu.edu/~salzberg/announce-oc1.html

ODBCMINE, analyzes ODBC databases using C4.5, and outputs simple IF..ELSE decision rules in ascii.
http://www.intsysr.com/odbcmine.htm

PC4.5, a parallel version of C4.5 built with Persistent Linda (PLinda) system.
http://www.cs.nyu.edu/~binli/pc4.5/

SMILES, advanced decision tree learner, with new splitting criteria, non-greedy search, extraction of different solutions, boosting, cost-sensitive learning, and more.
http://www.dsic.upv.es/~flip/smiles/

Random forests from Leo Breiman, a combination of tree predictors such that each tree depends on the values of a random vector sampled independently and with the same distribution for all trees in the forest.
http://www.stat.berkeley.edu/users/breiman

ps: eyounx不保证链接有效性，无效的话不要找我

Hybrid decision tree
http://cs.nju.edu.cn/people/zhouzh/zhouzh.files/publication/kbs02.pdf