Surveying the complementary role of automatic data analysis and visualization in knowledge discovery

ABSTRACT

The aim of this work is to survey and reflect on the various ways to integrate visualization and data mining techniques toward a mixed-initiative knowledge discovery taking the best of human and machine capabilities. Following a bottom-up bibliographic research approach, the article categorizes the observed techniques in classes, highlighting current trends, gaps, and potential future directions for research. In particular it looks at strengths and weaknesses of information visualization and data mining, and for which purposes researchers in infovis use data mining techniques and reversely how researchers in data mining employ infovis techniques. The article further uses this information to analyze the discovery process by comparing the analysis steps from the perspective of information visualization and data mining. The comparison permits to bring to light new perspectives on how mining and visualization can best employ human and machine skills.

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INDEX TERMS

Primary Classification:
  H. Information Systems
  H.5 INFORMATION INTERFACES AND PRESENTATION (I.7)
      H.5.2 User Interfaces (D.2.2, H.1.2, I.3.6)

Additional Classification:
  H. Information Systems
  H.1 MODELS AND PRINCIPLES
      H.1.2 User/Machine Systems
  H.2 DATABASE MANAGEMENT
      H.2.8 Database applications

General Terms:
Human Factors

Keywords:
data mining, human-machine interaction, knowledge discovery, survey, visual analytics, visual data mining, visualization