The role of visualization in effective data cleaning

Authors:
Yu Qian;Kang Zhang
Affiliations:
The University of Texas at Dallas, Richardson, TX;The University of Texas at Dallas, Richardson, TX
Venue:
Proceedings of the 2005 ACM symposium on Applied computing
Year:
2005

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Abstract

Using visualization techniques to assist conventional data mining tasks has attracted considerable interest in recent years. This paper addresses a challenging issue in the use of visualization for data mining: choosing appropriate parameters for spatial data cleaning methods. On one hand, algorithm performance is improved through visualization. On the other hand, characteristics and properties of methods and features of data are visualized as feedbacks to the user. A 3-D visualization model, called Waterfall, is proposed to assist spatial data cleaning in four important aspects: dimension-independent data visualization, visualization of data quality, algorithm parameter selection, and measurement of noise removing methods on parameter sensitiveness.