Classification aims to find rules for the classification of new data.
The mining approach is a supervised method, and some useful information needs to be extracted from the data clusters of a given type as the basis for the classification of new data
Most classification mining approaches find the rules and sort them into an easy to operate structure called the classifier.
The decision tree (Quinlan, 2003), proposed by Quinlan in 1992, is often used.
The decision tree uses the distribution differences of different types of data as the classification criterion, and thus, the classification rules extracted from the decision tree can use these characteristics to classify the new data.
In addition, the decision tree algorithm can find classification rules and convert them into a tree structure.
With the tree structure, given rules can be used to classify new data more rapidly because the decision tree uses the classifier produced from the classification mining method.