Sihan Pan
ABSTRACT
Ranking data items is a core step of decision making in many scenarios, such as investigating companies in finance and evaluating players in sports. While sorting by a single attribute is often trivial, ranking based on multi-attribute is often fuzzy, meaning that the goals, constraints, and weights of factors are not well-defined. Existing techniques use aggregation or dimension reduction to map the data along a single axis, which destroys the high-dimensional structure of data. In this paper, we aim to reduce the scope of data instead of the dimensionality in ranking tasks. In this way, users make decisions among a few data items based on complete information, instead of ranking many items based on inaccurate information. We introduce the concept of Pareto frontier for partitioning the data into multiple groups. A visual analytic system with two coordinated views is designed for users to rank data in an individual group or compare items in multiple groups. We evaluate the effectiveness of the proposed system through case studies and usability through a user study.
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Index Terms
- A Visual Analytic System for Ranking Multi-attribute Data Using Multi-level Pareto Frontier
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