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Proceedings Paper

Effects of cognitive styles and data characteristics on visual data mining
Author(s): Peter Bak; Joachim Meyer
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Paper Abstract

Visual display of information in data mining can support successful knowledge discovery. An experiment was conducted to identify parameters that affect the detection of cause-and-effect relations in time series data in a visual data mining environment. Accuracy of performance and the frequency of tool usage were measured as a function of visual properties of the cause-function and information processing styles. Performance accuracy differed between participants with different cognitive styles. Participants with high analytic cognitive style were better able to detect cause-and-effect relations through the investigation of visual and more global properties of the displayed data. Visual properties of the data affected users with high analytic and low experiential cognitive styles similarly and had no direct effect on accuracy. Participants with different levels of cognitive style differed in tool usage, indicating diverse approaches to solving the experimental task. The results point to the need to consider the effects of user characteristics and properties of the displayed data when designing visual data mining environments that are based on intense interaction of users with complex graphical displays.

Paper Details

Date Published: 11 March 2005
PDF: 10 pages
Proc. SPIE 5669, Visualization and Data Analysis 2005, (11 March 2005); doi: 10.1117/12.586812
Show Author Affiliations
Peter Bak, Ben-Gurion Univ. of the Negev (Israel)
Joachim Meyer, Ben-Gurion Univ. of the Negev (Israel)


Published in SPIE Proceedings Vol. 5669:
Visualization and Data Analysis 2005
Robert F. Erbacher; Jonathan C. Roberts; Matti T. Grohn; Katy Borner, Editor(s)

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