Inventing discovery tools: Combining information visualization with data mining

Abstract

The growing use of information visualization tools and data mining algorithms stems from two separate lines of research. Information visualization researchers believe in the importance of giving users an overview and insight into the data distributions, while data mining researchers believe that statistical algorithms and machine learning can be relied on to find the interesting patterns. Genomics researchers, financial analysts, and social scientists hunt for patterns in vast data warehouses using increasingly powerful software tools. These tools are based on emerging concepts, such as knowledge discovery, data mining, and information visualization. They also employ specialized methods, such as neural networks, decisions trees, principal components analysis, and a hundred others. The chapter discusses two issues that influence the design of discovery tools: statistical algorithms vs. visual data presentation and hypothesis testing vs. exploratory data analysis. A combined approach could lead to novel discovery tools that preserve user control, enable more effective exploration, and promote responsibility.