Visual Exploration of Large Scatter Plot Matrices by Pattern Recommendation based on Eye Tracking

ABSTRACT

The Scatter Plot Matrix (SPLOM) is a well-known technique for visual analysis of high-dimensional data. However, one problem of large SPLOMs is that typically not all views are potentially relevant to a given analysis task or user. The matrix itself may contain structured patterns across the dimensions, which could interfere with the investigation for unexplored views. We introduce a new concept and prototype implementation for an interactive recommender system supporting the exploration of large SPLOMs based on indirectly obtained user feedback from user eye tracking. Our system records the patterns that are currently under exploration based on gaze times, recommending areas of the SPLOM containing potentially new, unseen patterns for successive exploration. We use an image-based dissimilarity measure to recommend patterns that are visually dissimilar to previously seen ones, to guide the exploration in large SPLOMs. The dynamic exploration process is visualized by an analysis provenance heatmap, which captures the duration on explored and recommended SPLOM areas. We demonstrate our exploration process by a user experiment, showing the indirectly controlled recommender system achieves higher pattern recall as compared to fully interactive navigation using mouse operations.

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Index Terms

• Computing methodologies

  • Artificial intelligence

    • Philosophical/theoretical foundations of artificial intelligence

      • Cognitive science

• Human-centered computing

  • Human computer interaction (HCI)

• Information systems

  • Information retrieval

    • Evaluation of retrieval results

      • Relevance assessment