Neuware -Data Mining and Multi agent Integration aims to re ect state of the art research and development of agent mining interaction and integration (for short, agent min ing). The book was motivated by increasing interest and work in the agents data min ing, and vice versa. The interaction and integration comes about from the intrinsic challenges faced by agent technology and data mining respectively; for instance, multi agent systems face the problem of enhancing agent learning capability, and avoiding the uncertainty of self organization and intelligence emergence. Data min ing, if integrated into agent systems, can greatly enhance the learning skills of agents, and assist agents with predication of future states, thus initiating follow up action or intervention. The data mining community is now struggling with mining distributed, interactive and heterogeneous data sources. Agents can be used to man age such data sources for data access, monitoring, integration, and pattern merging from the infrastructure, gateway, message passing and pattern delivery perspectives. These two examples illustrate the potential of agent mining in handling challenges in respective communities. There is an excellent opportunity to create innovative, dual agent mining interac tion and integration technology, tools and systems which will deliver results in one new technology.Springer Verlag GmbH, Tiergartenstr. 17, 69121 Heidelberg 348 pp. Englisch. N� de ref. del art�culo 9781489984401

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T�tulo: Data Mining and Multi-agent Integration

Editorial: Springer US, Springer US Feb 2015

A�o de publicaci�n: 2015

Encuadernaci�n: Taschenbuch

Condici�n: Neu

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Sinopsis:

Data Mining and Multi agent Integration aims to re?ect state of the art research and development of agent mining interaction and integration (for short, agent min ing). The book was motivated by increasing interest and work in the agents data min ing, and vice versa. The interaction and integration comes about from the intrinsic challenges faced by agent technology and data mining respectively; for instance, multi agent systems face the problem of enhancing agent learning capability, and avoiding the uncertainty of self organization and intelligence emergence. Data min ing, if integrated into agent systems, can greatly enhance the learning skills of agents, and assist agents with predication of future states, thus initiating follow up action or intervention. The data mining community is now struggling with mining distributed, interactive and heterogeneous data sources. Agents can be used to man age such data sources for data access, monitoring, integration, and pattern merging from the infrastructure, gateway, message passing and pattern delivery perspectives. These two examples illustrate the potential of agent mining in handling challenges in respective communities. There is an excellent opportunity to create innovative, dual agent mining interac tion and integration technology, tools and systems which will deliver results in one new technology.

De la contraportada:

Data Mining and Multi-agent Integration presents cutting-edge research, applications and solutions in data mining, and the practical use of innovative information technologies written by leading international researchers in the field. Topics examined include:

Integration of multiagent applications and data mining

Mining temporal patterns to improve agents behavior

Information enrichment through recommendation sharing

Automatic web data extraction based on genetic algorithms and regular expressions

A multiagent learning paradigm for medical data mining diagnostic workbench

A multiagent data mining framework

Streaming data in complex uncertain environments

Large data clustering

A multiagent, multi-objective clustering algorithm

Interactive web environment for psychometric diagnostics

Anomalies detection on distributed firewalls using data mining techniques

Automated reasoning for distributed and multiple source of data

Video contents identification

Data Mining and Multi-agent Integration is intended for students, researchers, engineers and practitioners in the field, interested in the synergy between agents and data mining. This book is also relevant for readers in related areas such as machine learning, artificial intelligence, intelligent systems, knowledge engineering, human-computer interaction, intelligent information processing, decision support systems, knowledge management, organizational computing, social computing, complex systems, and soft computing.

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