Longbing Cao; Ana L.C. Bazzan; Vladimir Gorodetsky; Pericles A. Mitkas; Gerhard Weiss; Philip S. Yu Springer-Verlag Berlin and Heidelberg GmbH & Co. KG (2010) Pehmeäkantinen kirja
Longbing Cao; Ana L.C. Bazzan; Andreas L. Symeonidis; Vladimir Gorodetsky; Gerhard Weiss; Philip S. Yu Springer-Verlag Berlin and Heidelberg GmbH & Co. KG (2012) Pehmeäkantinen kirja
Longbing Cao; Yifeng Zeng; Andreas L. Symeonidis; Vladimir Gorodetsky; Philip S. Yu; Munindar P. Singh Springer-Verlag Berlin and Heidelberg GmbH & Co. KG (2013) Pehmeäkantinen kirja
Longbing Cao (ed.); Hiroshi Motoda (ed.); Jaideep Srivastava (ed.); Ee-peng Lim (ed.); Irwin King (ed.); Philip S. (ed. Yu Springer (2013) Pehmeäkantinen kirja
Longbing Cao; Yifeng Zeng; Andreas L. Symeonidis; Vladimir Gorodetsky; Jörg P. Müller; Philip S. Yu Springer-Verlag Berlin and Heidelberg GmbH & Co. KG (2014) Pehmeäkantinen kirja
Longbing Cao (ed.); Yifeng Zeng (ed.); Bo An (ed.); Andreas L. Symeonidis (ed.); Vladimir Gorodetsky (ed.); Frans ( Coenen Springer (2015) Pehmeäkantinen kirja
Springer Sivumäärä: 334 sivua Asu: Kovakantinen kirja Painos: 2009 Julkaisuvuosi: 2009, 04.08.2009 (lisätietoa) Kieli: Englanti
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.