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International Journal of Human-Computer Studies
Volume 65, Issue 2, February 2007, Pages 95-110
 
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doi:10.1016/j.ijhcs.2006.07.005    How to Cite or Link Using DOI (Opens New Window)
Copyright © 2006 Published by Elsevier Ltd.

Design and evaluation of visualization support to facilitate decision trees classification

Yan Liua, Corresponding Author Contact Information, E-mail The Corresponding Author and Gavriel Salvendyb, c

aDepartment of Biomedical, Industrial, and Human Factors Engineering, Wright State University, Dayton, OH 45435, USA bSchool of Industrial Engineering, Purdue University, West Lafayette, IN 47906, USA cDepartment of Industrial Engineering, Tsinghua University, Beijing 100084, PR China

Received 13 July 2005; 
revised 14 July 2006; 
accepted 17 July 2006. 
Communicated by J.D. Fekete. 
Available online 7 November 2006.

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Abstract

The loosely coupled relationships between visualization and analytical data mining (DM) techniques represent the majority of the current state of art in visual data mining; DM modeling is typically an automatic process with very limited forms of guidance from users. A conceptual model of the visualization support to DM modeling process and a novel interactive visual decision tree (IVDT) classification process have been proposed in this paper, with the aim of exploring humans’ pattern recognition ability and domain knowledge to facilitate the knowledge discovery process. An IVDT for categorical input attributes has been developed and experimented on 20 subjects to test three hypotheses regarding its potential advantages. The experimental results suggested that, compared to the automatic modeling process as typically applied in current decision tree modeling tools, IVDT process can improve the effectiveness of modeling in terms of producing trees with relatively high classification accuracies and small sizes, enhance users’ understanding of the algorithm, and give them greater satisfaction with the task.

Keywords: Visual data mining; Interactive modeling; Model visualization; Data visualization

Article Outline

1. Introduction
2. Proposed conceptual model of visualization support to data mining modeling process
3. Overview of decision trees classification
4. Related work on visualization support to decision trees classification
4.1. Visualization of decision trees
4.2. Interactive decision trees classification
5. Proposed design of interactive visual decision tree classification
6. Experiment
6.1. Derivation of hypotheses
6.2. Subjects
6.3. Independent variables
6.4. Dependent variables
6.4.1. Effectiveness of the modeling
6.4.2. Understanding of algorithms
6.4.3. Satisfaction with task
6.5. Experiment design
6.6. Procedure
6.7. Experimental results
6.7.1. Hypothesis I
6.7.2. Hypothesis II
6.7.3. Hypothesis III
7. Conclusions and future research
Appendix A. Algorithm understanding questionnaire
Appendix B. Modeling satisfaction questionnaire
References








 
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