Discovering a Learning Module for Poker’s Rule through Data Mining Algorithms

Data mining classification methods can be a powerful tool when it comes to learning card game rules such as Poker. There are millions of possible combination in the game and making a decision tree to cover all the rules is not desirable. We used the J48 decision tree model of data mining software Weka and made parameter analysis. Then we show experimentally how the number of instances is affecting the correctness of the classification, and propose an equation to determine accuracy based on the number of instances in a data set. We examine several different attributes and the experiment shows high performance.