During exploratory data analysis in a variety of domains we have found it useful to summarize the available data via sets of rules and their exceptions. Often, a practitioner is interested in exploring more than one such summary before settling on any single explanation. It is important for each summary to be succinct, cover a significant fraction of the data as well as highlight rules enjoying high confidence, support and lift. It is often difficult to achieve all these objectives simultaneously, and retain a meaningful dialogue with the user. To help achieve these goals, we have found it useful to combine some existing tools for data analysis: association rule-mining to find broad patterns in the data; clustering to group these patterns into similar sets; exception-mining to find deviations from the patterns; and a coverage-based extraction of explanations for the data. We demonstrate how a combination of these methods can be used for exploratory data analysis on both synthetic and industrial data sets for which we derived interesting insights.
['Sarmimala Saikia', 'Gautam Shroff', 'Puneet Agarwal', 'Ashwin Srinivasan', 'Aditeya Pandey', 'Gaurangi Anand']
Exploratory data analysis using alternating covers of rules and exceptions