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V-Miner: using enhanced parallel coordinates to mine product design and test data by Kaidi Zhao; Bing Liu; Thomas M. Tirpak; Andreas Schaller is a scholarly article available to read on EtoBox.

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Analyzing data to find trends, correlations, and stable patterns is an important task in many industrial applications. This paper proposes a new technique based on parallel coordinate visualization. Previous work on parallel coordinate methods has shown that they are effective only when variables that are correlated and/or show similar patterns are displayed adjacently. Although current parallel coordinate tools allow the user to manually rearrange the order of variables, this process is very time-consuming when the number of variables is large. Automated assistance is required. This paper introduces an editdistance based technique to rearrange variables so that interesting change patterns can be easily detected visually. The Visual Miner (V-Miner) software includes both automated methods for visualizing common patterns and a query tool that enables the user to describe specific target patterns to be mined or displayed by the system. In addition, the system can filter data according to rules sets imported from other data mining tools. This feature was found very helpful in practice, because it enables decision makers to visually identify interesting rules and data segments for furt

Author

Kaidi Zhao; Bing Liu; Thomas M. Tirpak; Andreas Schaller

Publisher

ACM

Published

2004

Language

EN