决策树的学习资源

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:AnInformalTutorial
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
BuildclassificationtreeinExcelusingC4.5
http://www.geocities.com/adotsaha/CTree/CtreeinExcel.html

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

SampleApplicationsUsingSee5/C5.0
PredictingMagneticPropertiesofCrystals
ProfilingHighIncomeEarnersfromCensusData
AssessingChurnRisk
DetectingAdvertisementsontheWeb
IdentifyingSpam
DiagnosingHypothyroidism
NowReadOn...
http://www.rulequest.com/see5-examples.html

FreeDecisionTreeSoftwareforClassification
freeandshareware:

C4.5,the"classic"decision-treetool,developedbyJ.R.Quinlan,(restricteddistribution)
http://www.cse.unsw.edu.au/~quinlan/

ClassificationTreeinExcel,fromAngshumanSaha
http://www.geocities.com/adotsaha/CTree/CtreeinExcel.html

IND,providesGiniandC4.5styledecisiontreesandmore.PubliclyavailablefromNASAbutwithexportrestrictions.
http://ic.arc.nasa.gov/projects/bayes-group/ind/IND-program.html

LMDT,buildsLinearMachineDecisionTrees(basedonBrodleyandUtgoffpapers).
http://mow.ecn.purdue.edu/~brodley/software/lmdt.html

OC1,decisiontreesystemcontinuousfeaturevalues;buildsdecisiontreeswithlinearcombinationsofattributesateachinternalnode;thesetreesthenpartitionthespaceofexampleswithbothobliqueandaxis-parallelhyperplanes.
http://www.cs.jhu.edu/~salzberg/announce-oc1.html

ODBCMINE,analyzesODBCdatabasesusingC4.5,andoutputssimpleIF..ELSEdecisionrulesinascii.
http://www.intsysr.com/odbcmine.htm

PC4.5,aparallelversionofC4.5builtwithPersistentLinda(PLinda)system.
http://www.cs.nyu.edu/~binli/pc4.5/

SMILES,advanceddecisiontreelearner,withnewsplittingcriteria,non-greedysearch,extractionofdifferentsolutions,boosting,cost-sensitivelearning,andmore.
http://www.dsic.upv.es/~flip/smiles/

RandomforestsfromLeoBreiman,acombinationoftreepredictorssuchthateachtreedependsonthevaluesofarandomvectorsampledindependentlyandwiththesamedistributionforalltreesintheforest.
http://www.stat.berkeley.edu/users/breiman