Agents and Data Mining Interaction: 7th International by Peter Stone (auth.), Longbing Cao, Ana L. C. Bazzan, Andreas

By Peter Stone (auth.), Longbing Cao, Ana L. C. Bazzan, Andreas L. Symeonidis, Vladimir I. Gorodetsky, Gerhard Weiss, Philip S. Yu (eds.)

This ebook constitutes the completely refereed post-workshop lawsuits of the seventh foreign Workshop on brokers and information Mining interplay, ADMI 2011, held in Taipei, Taiwan, in may possibly 2011 along side AAMAS 2011, the tenth foreign Joint convention on self reliant brokers and Multiagent structures.
The eleven revised complete papers offered have been conscientiously reviewed and chosen from 24 submissions. The papers are prepared in topical sections on brokers for info mining; facts mining for brokers; and agent mining applications.

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Additional resources for Agents and Data Mining Interaction: 7th International Workshop on Agents and Data Mining Interation, ADMI 2011, Taipei, Taiwan, May 2-6, 2011, Revised Selected Papers

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5 Operation The operation of the proposed MADM clustering mechanism is described in this section. We have elected to describe the operation in a procedural manner as we believe this will facilitates understanding (although readers should be clear that the operation is anything but procedural). As noted in the foregoing, Clustering Agents are spawned by a User Agent according to the nature of the end user’s initial “clustering request”. Fundamentally there are two strategies for spawning Clustering Agents: the K-means strategy and the KNN strategy.

Groups of clustering agents can be though of as representing a clustering algorithm. With respect to this paper the K-means [22] and K-Nearest Neighbour (KNN) [10] clustering algorithms have been adopted; however, our collections of clustering agents could have been configured to perform some alternative form of clustering (for example hierarchical clustering). A number of clustering agents will be spawned, as required, by a user agent in order to perform some clustering task. Thus, each clustering agent represents a cluster and is responsible for selecting a record from a data set and determining whether that record would belong to its cluster or not.

D. User agent: It communicates with the task agent to assign the task and get the results back. Gongzhu Hu and Shaozhen Ding. [35,36] proposed an agent based approach to mine the association rules from the distributed data sets across the multiple sites while preserving the privacy of the local data sets. This approach relies on the local systems to Agent Enriched Distributed Association Rules Mining: A Review 39 find the frequent itemsets that are encrypted and the partial results are carried from site to site.

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