classical classification of data mining algorithms, evolved from the ID3 algorithm. This is mainly used to deal with continuous attribute values, the basic process is as follows : 1. According to the values of attributes of sorting two data sets. different thresholds of data sets dynamic classification 3. According iteration threshold Progressive- draw 4. all possible threshold, and gains than Gain

Data
....\crx.data
....\crx.names
....\crx.test
....\golf.data
....\golf.names
....\hypo.data
....\hypo.names
....\hypo.test
....\labor-neg.data
....\labor-neg.names
....\labor-neg.test
....\monk1.data
....\monk1.names
....\monk1.test
....\monk2.data
....\monk2.names
....\monk2.test
....\monk3.data
....\monk3.names
....\monk3.test
....\soybean.data
....\soybean.names
....\vote.data
....\vote.names
....\vote.test
Src
...\average.c
...\besttree.c
...\build.c
...\buildex.i
...\c4.5.c
...\c4.5rules.c
...\classify.c
...\confmat.c
...\consult.c
...\consultr.c
...\contin.c
...\defns.i
...\discr.c
...\extern.i
...\genlogs.c
...\genrules.c
...\getdata.c
...\getnames.c
...\getopt.c
...\header.c
...\info.c
...\Makefile
...\makerules.c
...\Modifications
...\prune.c
...\prunerule.c
...\rules.c
...\rulex.i
...\siftrules.c
...\sort.c
...\st-thresh.c
...\stats.c
...\subset.c
...\testrules.c
...\trees.c
...\types.i
...\userint.c
...\xval-prep.c
...\xval.sh

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