Abstract
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).
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Preview
Unable to display preview. Download preview PDF.
References
Dawyer, K.: Decision Tree Instability and Active Learning. MSc. thesis, Department of Computing Science, University of Alberta, Edmonton, AB, Canada, Spring (2007)
Han, J., Kamber, M.: Data Mining: Concepts and Techniques, 2nd edn. Morgan Kaufman publishers, San Francisco (2008)
Shawe-Taylor, J., Christianini, N.: Support Vector Machines and other kernel based learning methods. Cambridge University Press, Cambridge (2000)
Haykin, S.: Neural Networks: A Comprehensive Foundation, 2nd edn. Prentice-Hall, Englewood Cliffs (1999)
Mitchell, T.: Machine Learning. McGraw Hill, New York (1997)
Rokach, L., Maimon, O.: Data Mining with Decision trees: Theory and Applications. Series in Machine Perception and Artifical Intelligence, vol. 69. World Scientific Publishing, Singapore (2008)
Cios, K.J., Pedrycz, W., Swiniarski, R.W., Kurgan, L.A.: Data Mining: A Knowledge Discovery Approach. Springer, Heidelberg (2007)
Breiman, L., Friedman, J.H., Olshen, R.A., Stone, C.J.: Classification and Regression Trees, 1st edn. Taylor & Francis, Abington (1984)
Tan, P.-N., Steinbach, M., Kumar, V.: Introduction to Data Mining. Addison-Wesley, Reading (2005)
Duda, R.O., Hart, P.E., Stork, D.G.: Pattern Classification. Wiley, Chichester (1973)
Quinlan, J.R.: C4.5: Programs for Machine Learning. Morgan Kaufmann Series in Machine Learning (1992)
Quinlan, J.R.: Induction of decision trees. Machine Learning Archive 1(1), 81–106 (1986)
Mingers, J.: An Empirical Comparison of Pruning Methods for Decision Tree Induction. Machine Learning archive 4(2), 227–243 (1989)
Hall, M., Frank, E., Holmes, G., Pfahringer, B., Reutemann, P., Witten, I.H.: The WEKA Data Mining Software: An Update. SIGKDD Explorations 11(1) (2009)
http://imacwww.epfl.ch/Team/Raphael/BookWiley2003/java-illustrations/ID3Standard/ID3.java.html
http://www2.cs.uregina.ca/~dbd/cs831/notes/ml/dtrees/c4.5/tutorial.html
http://weka.wikispaces.com/Explorer+tree+visualization+plugins
Available on request. Please email to the corresponding author
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2010 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Bhatnagar, V., Zaman, E., Rajpal, Y., Bhardwaj, M. (2010). VisTree: Generic Decision Tree Inducer and Visualizer. In: Kikuchi, S., Sachdeva, S., Bhalla, S. (eds) Databases in Networked Information Systems. DNIS 2010. Lecture Notes in Computer Science, vol 5999. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-12038-1_15
Download citation
DOI: https://doi.org/10.1007/978-3-642-12038-1_15
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-12037-4
Online ISBN: 978-3-642-12038-1
eBook Packages: Computer ScienceComputer Science (R0)