Visual Association Rules
Association rule visualization
Association rule mining finds frequent associations between sets of items in a large number of transactions. In real world applications, it often produces too many rules for humans to read over. The answer to this problem is to select the most interesting rules. As interestingness is a subjective measure, selecting the most interesting rules is inherently human being’s work. It is thus expected that information visualization may play an important role in managing a large number of discovered association rules and in identifying the most interesting ones.
An association rule reflects a many-to-many relationship. In market basket analysis, a typical association rule reads: 80% of transactions that buy diapers and milk also buy beer and chips. The rule is supported by 10% of all transactions. In lacking of an effective visual metaphor to display many-to-many relationships, information visualization has received a...
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