With the growing adoption of machine learning techniques, there is a surge of research interest towards making machine learning systems more transparent and interpretable. Various visualizations have been developed to help model developers understand, diagnose, and refine machine learning models. However, a large number of potential but neglected users are the domain experts with little knowledge of machine learning but are expected to work with machine learning systems. In this paper, we present an interactive visualization technique to help users with little expertise in machine learning to understand, explore and validate predictive models. By viewing the model as a black box, we extract a standardized rule-based knowledge representation from its input-output behavior. Then, we design RuleMatrix, a matrix-based visualization of rules to help users navigate and verify the rules and the black-box model. We evaluate the effectiveness of RuleMatrix via two use cases and a usability study.

In this paper, we introduce a visualization method that couples a trend chart with word clouds to illustrate temporal content evolutions in a set of documents. Specifically, we use a trend chart to encode the overall semantic evolution of document content over time. In our work, semantic evolution of a document collection is modeled by varied significance of document content, represented by a set of representative keywords, at different time points. At each time point, we also use a word cloud to depict the representative keywords. Since the words in a word cloud may vary one from another over time (e.g., words with increased importance), we use geometry meshes and an adaptive force-directed model to lay out word clouds to highlight the word differences between any two subsequent word clouds. Our method also ensures semantic coherence and spatial stability of word clouds over time. Our work is embodied in an interactive visual analysis system that helps users to perform text analysis and derive insights from a large collection of documents. Our preliminary evaluation demonstrates the usefulness and usability of our work.

sequence visualization based on aggregate information to analyze the behavior of an RNN's hidden state at the sentence-level. The usability and effectiveness of our method are demonstrated through case studies and reviews from domain experts.

Parallel coordinates have been widely applied to visualize high-dimensional and multivariate data, discerning patterns within the data through visual clustering. However, the effectiveness of this technique on large data is reduced by edge clutter. In this paper, we present a novel framework to reduce edge clutter, consequently improving the effectiveness of visual clustering. We exploit curved edges and optimize the arrangement of these curved edges by minimizing their curvature and maximizing the parallelism of adjacent edges. The overall visual clustering is improved by adjusting the shape of the edges while keeping their relative order. The experiments on several representative datasets demonstrate the effectiveness of our approach.