Methods and systems for using clustering for splitting tree nodes in classification decision trees

A computer-implemented method, comprising:

• receiving, by a computing device, input data related to a decision tree to be generated from a data set, wherein the input data identifies a target attribute of the data set and a set of candidate attributes of the data set to be used as nodes in the decision tree;
• determining, by the computing device, using a clustering algorithm and the set of candidate attributes, a plurality of potential splitting schemes to be used to split a node in the decision tree;
• calculating, by the computing device, a splitting measurement for each of the plurality of potential splitting schemes;
• and
• selecting, by the computing device, an optimal splitting scheme from the plurality of potential splitting schemes for each node in the decision tree based on the splitting measurement.

A system, comprising:

• a processor;
• and
• a non-transitory computer-readable storage medium including instructions configured to be executed that, when executed by the processor, cause the system to perform operations including:
• receiving input data related to a decision tree to be generated from a data set, wherein the input data identifies a target attribute of the data set and a set of candidate attributes of the data set to be used as nodes in the decision tree;
• determining using a clustering algorithm and the set of candidate attributes, a plurality of potential splitting schemes to be used to split a node in the decision tree;
• calculating a splitting measurement for each of the plurality of potential splitting schemes;
• and
• selecting an optimal splitting scheme from the plurality of potential splitting schemes for each node in the decision tree based on the splitting measurement.