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Do you know where can I find some information of this algorithm, to study it??. Is there already an example of its implementation, or, only Quinlan knows its implementation??
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C5. 0 can produce two kinds of models. A decision tree is a straightforward description of the splits found by the algorithm. Each terminal (or "leaf") node describes a particular subset of the training data, and each case in the training data belongs to exactly one terminal node in the tree.
C5 - COUNTER - Is a data type made just for the counter instructions and are made up of 3 integers organized so you can access parts of it as bits and parts of it as words.
At each node of the tree, C4. 5 chooses the attribute of the data that most effectively splits its set of samples into subsets enriched in one class or the other. The splitting criterion is the normalized information gain (difference in entropy).
The J48 implementation of the C4. 5 algorithm has many additional features including accounting for missing values, decision trees pruning, continuous attribute value ranges, derivation of rules, etc. In the WEKA data mining tool, J48 is an open-source Java implementation of the C4.
His company, rulequest, has it: http://rulequest.com/GPL/C50.tgz
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C5.0 which Quinlan is commercially selling is an improvement on C4.5.
According to this sentence from the wikipedia page this algorithm is not open for everyone. I guess it will take some effort to get something going by yourself.
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The single threaded version is available under the GPL license. :)
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