C4.5: programs for machine learning
C4.5: programs for machine learning
An introduction to support Vector Machines: and other kernel-based learning methods
An introduction to support Vector Machines: and other kernel-based learning methods
Neural Networks: A Comprehensive Foundation
Neural Networks: A Comprehensive Foundation
Machine Learning
Machine Learning
Data Mining: Concepts and Techniques
Data Mining: Concepts and Techniques
Introduction to Data Mining, (First Edition)
Introduction to Data Mining, (First Edition)
Data Mining: A Knowledge Discovery Approach
Data Mining: A Knowledge Discovery Approach
The WEKA data mining software: an update
ACM SIGKDD Explorations Newsletter
Data Mining with Decision Trees: Theroy and Applications
Data Mining with Decision Trees: Theroy and Applications
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Decision tree is one of the most popular and commonly used technique for predictive modeling. Interpretability and understandability makes decision trees an attractive option among various classification induction algorithms. There are several freewares available for decision tree induction which can be used in data mining education and practice. However, these freewares have limited capability to interactively visualize the induced tree and experiment with the induction process. In this paper, we describe the design of a generic decision tree inducer and visualizer which gives options for multiple splitting criteria (e.g. information gain, gain ratio etc.) and pruning criteria (e.g. minimum error pruning, cost complexity pruning etc.) for decision tree induction. The induced tree can be visualized interactively by the user and even saved for future visualization and comparison with another tree. These options are available through a user friendly GUI. The performance statistics for the induced tree can also be viewed by the user. The package has been designed using open source softwares including JDK 1.6, Netbeans 6.5 and Prefuse (for visualization of the constructed tree).