Advances in Artificial Intelligence: 14th Biennial by Aijun An, Nick Cercone, Xiangji Huang (auth.), Eleni PDF

By Aijun An, Nick Cercone, Xiangji Huang (auth.), Eleni Stroulia, Stan Matwin (eds.)

ISBN-10: 3540421440

ISBN-13: 9783540421443

ISBN-10: 3540451536

ISBN-13: 9783540451532

AI 2001 is the 14th within the sequence of Arti cial Intelligence meetings subsidized via the Canadian Society for Computational reviews of Intelligence/Soci et e - nadienne pour l’ etude de l’intelligence par ordinateur. As used to be the case final 12 months too, the convention is being held along with the yearly meetings of 2 different Canadian societies, pictures Interface (GI 2001) and imaginative and prescient Int- face (VI 2001). We think that the final event should be enriched through this conjunction of meetings. This yr is the \silver anniversary" of the convention: the rst Canadian AI convention was once held in 1976 at UBC. in the course of its lifetime, it has attracted Canadian and foreign papers of top of the range from numerous AI examine components. All papers submitted to the convention acquired not less than 3 indep- dent reports. nearly one 3rd have been accredited for plenary presentation on the convention. the easiest paper of the convention might be invited to seem in Computational Intelligence.

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Extra resources for Advances in Artificial Intelligence: 14th Biennial Conference of the Canadian Society for Computational Studies of Intelligence, AI 2001 Ottawa, Canada, June 7–9, 2001 Proceedings

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The infrastructure is made up of three parts that all are holonicly structured. Two of them, namely the user-supporting co-ordination assistant agent (CA) and the auctionperforming auction matchmaker agent (AMM) are holonic agents that are hierarchically structured. The requests of the CAs in the market environments are brought together with the services offered by several AMMs by the matching services of facilitator (FA) and domain matchmaker agents (DMM). FAs and DMMs integrate CAs and AMMs in a mediation holon which is organized as a networked federation of trusted agents.

A Low-Scan Incremental Association Rule Maintenance Method 35 References [1] Agrawal, R. : Fast Algorithms for Mining Association Rules in Large Databases, In Proceedings of the 20th International Conference on very Large Databases, Santiago, Chile, 1994, pp. 487-499. : Maintenance of Discovered Association Rules in Large Database: An I ncremental Updating Technique, In Proceedings of the 12th International Conference on Data Engineering, New Orleans, Louisiana, 1996. : A General Incremental Technique for Maintaining Discovered Association Rules, In Proceedings of the Fifth International Conference on Database Systems for Advanced Applications, Melbourne, Australia, jan, 1997.

Association rule mining is a data mining technique which discovers strong associations or correlation relationships among data. Given a set of transactions (similar to database records in this context), where each transaction consists of items (or attributes), an association rule is an implication of the form X → Y , where X and Y are sets of items and X ∩ Y = ∅. The support of this rule is defined as the percentage of transactions that contain the set X, while its confidence is the percentage of these “X” transactions that also contain Y.

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Advances in Artificial Intelligence: 14th Biennial Conference of the Canadian Society for Computational Studies of Intelligence, AI 2001 Ottawa, Canada, June 7–9, 2001 Proceedings by Aijun An, Nick Cercone, Xiangji Huang (auth.), Eleni Stroulia, Stan Matwin (eds.)


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