Detecting Patterns of Change Using Enhanced Parallel Coordinates Visualization

Analyzing data to find trends, correlations, and stable patterns is an important problem for many industrial applications. In this paper, we propose a new technique based on parallel coordinates visualization. Previous work on parallel coordinates methods has shown that they are effective only when variables that are correlated and/or show similar patterns are displayed adjacently. Although current parallel coordinates 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 needed. This paper proposes an edit-distance based technique to rearrange variables so that interesting patterns can be easily detected. Our system, V-Miner, includes both automated methods for visualizing common patterns and a query tool that enables the user to describe specific target patterns to be mined/displayed by the system. Following an overview of the system, a case study is presented to explain how Motorola engineers have used V-Miner to identify significant patterns in their product test and design data.