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On this page, we list a few ways in which careful visualization designs can facilitate more statistically sound exploratory data analysis. | On this page, we list a few ways in which careful visualization designs can facilitate more statistically sound exploratory data analysis. | ||
* Using user knowledge to inform hypothesis partition | * 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 | * 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 | |||
Latest revision as of 18:34, 27 February 2017
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
- Using user knowledge to inform hypothesis partition
- 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