Extending association rule discovery to numeric data

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This project tackles a key limitation of association-rule discovery, which is one of the main techniques used in data mining. Much valuable data is numeric. However, association-rule discovery cannot satisfactorily model numeric data, a limitation that has greatly restricted its application. This project investigates a novel new technique that overcomes this limitation. Impact-rule discovery finds associations with numeric distributions. This allows data analysts to discover precisely the type of information that they usually seek from numeric data, for example, how to maximize either average or aggregate measures of outcomes such as health, compliance, profit, or accuracy.