Use cases

On this page, we list a few ways in which careful visualization designs can facilitate more statistically sound exploratory data analysis.

• Visual verification of theoretically derived support threshold for frequent itemsets mining
  • Assumption: users might feel uncomfortable simply ignoring itemsets below a support threshold that is derived algorithmically
  • Proposed solutions: design an interface that visualizes aggregate properties of itemsets below the computed support threshold and example itemsets
• Engaging the user to reduce the space of hypotheses to be tested, e.g. include users in the hypothesis partition process
  • Using user knowledge to inform hypothesis partition
    • e.g. the user might be interested only in hypotheses that involve certain variables
  • Helping the user select subsets of hypotheses to test
    • e.g. display visual summary of each set of hypotheses
• User-guided construction of null hypothesis distribution corresponding to no interesting observations
• Help the user make decisions during iterative exploratory data analysis
  • e.g. choosing the appropriate alpha-investing policy