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 knowledge Mining interplay, ADMI 2011, held in Taipei, Taiwan, in may perhaps 2011 along with AAMAS 2011, the tenth foreign Joint convention on self sufficient brokers and Multiagent structures.
The eleven revised complete papers offered have been rigorously reviewed and chosen from 24 submissions. The papers are geared up in topical sections on brokers for information mining; info mining for brokers; and agent mining applications.
Read or Download 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 PDF
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Extra info 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
They cooperate, negotiate and collaborate with other agents to make a smart system. e. , preservation of intellectual property rights . Since MAS are also distributed systems, combining DDM with MAS for data intensive applications is appealing . A number of DDM solutions[23,24,25,26,27,28,29,37] are provided in recent years using various techniques such as distributed association rules, distributed clustering, Bayesian learning, classification (regression), and compression, but only a few of them make use of intelligent agents .
Once an agent is launched, it can continue to function even if the user is disconnected from the network. They implement a computational metaphor that is analogous to how most people conduct business in their daily lives: visit a place, use a service, and then move on. When an agent reaches a server, it is delivered to an agent execution environment. Then, if it possesses necessary authentication credentials, its executable parts are started. To accomplish its task, the mobile agent can transport itself to another server in search of the needed resource/service, spawn new agents, or interact with other stationary agents.
U. P. Kulkarni et. al. [41,42] suggested the improvements over the method proposed by You-Lin Ruan et al  and uses the advantages of MAs over client/server based approaches for better bandwidth usage and network latency. The methodology adopted in  uses two steps. 1. Mining Local Frequent Item sets (LFI) at each site in parallel and send them to central site to calculate Global Frequent Item sets(GFI). 2. Central site calculates the Candidate Global Frequent Item sets-CGFI and send them to all sites.
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 by Peter Stone (auth.), Longbing Cao, Ana L. C. Bazzan, Andreas L. Symeonidis, Vladimir I. Gorodetsky, Gerhard Weiss, Philip S. Yu (eds.)