Mining association rules between sets of items in large databases
SIGMOD '93 Proceedings of the 1993 ACM SIGMOD international conference on Management of data
Games That Agents Play: A Formal Framework for Dialogues between Autonomous Agents
Journal of Logic, Language and Information
Tree Structures for Mining Association Rules
Data Mining and Knowledge Discovery
Multi-party argument from experience
ArgMAS'09 Proceedings of the 6th international conference on Argumentation in Multi-Agent Systems
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In this paper we describe an approach to classifying objects in a domain where classifications are uncertain using a novel combination of argumentation and data mining. Classification is the topic of a dialogue game between two agents, based on an argument scheme and critical questions designed for use by agents whose knowledge of the domain comes from data mining. Each agent has its own set of examples which it can mine to find arguments based on association rules for and against a classification of a new instance. These arguments are exchanged in order to classify the instance. We describe the dialogue game, and in particular discuss the strategic considerations which agents can use to select their moves. Different strategies give rise to games with different characteristics, some having the flavour of persuasion dialogues and other deliberation dialogues.