Integration of Unsupervised Clustering, Interaction and Parallel Coordinates for the Exploration of Large Multivariate Data

Authors:
Jimmy Johansson;Robert Treloar;Mikael Jern
Affiliations:
Linköping University;Unilever Research;Linköping University
Venue:
IV '04 Proceedings of the Information Visualisation, Eighth International Conference
Year:
2004

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Abstract

Parallel coordinates are widely used in many applications for visualization of multivariate data. Because of the nature of parallel coordinates, the visualization technique is often used for data overview. However, when the number of tuples to be visualized becomes very large, this technique makes it difficult to distinguish the overall structure. In this paper we present a novel technique which uses a classification approach, the self-organizing map (an unsupervised learning algorithm), to solve this problem by creating an initial clustering of the data. By initially only visualizing the resulting representational clusters, the inherited global structure can be shown. Using linked views and allowing the user to perform drill-down and filtering on these representations reveals the single data items without loss of context.