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AGENTS '98 Proceedings of the second international conference on Autonomous agents
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COMPSAC '00 24th International Computer Software and Applications Conference
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AAMAS '04 Proceedings of the Third International Joint Conference on Autonomous Agents and Multiagent Systems - Volume 1
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IAT '06 Proceedings of the IEEE/WIC/ACM international conference on Intelligent Agent Technology
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Topological analysis of AOCD-based agent networks and experimental results
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ICNC '08 Proceedings of the 2008 Fourth International Conference on Natural Computation - Volume 01
Agent Mining: The Synergy of Agents and Data Mining
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An Experimental Study of Graph Connectivity for Unsupervised Word Sense Disambiguation
IEEE Transactions on Pattern Analysis and Machine Intelligence
Agent-mining interaction: an emerging area
AIS-ADM'07 Proceedings of the 2nd international conference on Autonomous intelligent systems: agents and data mining
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KES-AMSTA'10 Proceedings of the 4th KES international conference on Agent and multi-agent systems: technologies and applications, Part I
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ADMI'11 Proceedings of the 7th international conference on Agents and Data Mining Interaction
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IEEE Transactions on Fuzzy Systems
A topological description language for agent networks
APWeb'12 Proceedings of the 14th Asia-Pacific international conference on Web Technologies and Applications
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Wiley Interdisciplinary Reviews: Data Mining and Knowledge Discovery
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Fundamenta Informaticae - Concurrency Specification and Programming (CS&P 2003)
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Online social networks OSN are facing challenges since they have been extensively applied to different domains including online social media, e-commerce, biological complex networks, financial analysis, and so on. One of the crucial challenges for OSN lies in information overload and network congestion. The demands for efficient knowledge discovery and data mining methods in OSN have been rising in recent year, particularly for online social applications, such as Flickr, YouTube, Facebook, and LinkedIn. In this paper, a Belief-Desire-Intention BDI agent-based method has been developed to enhance the capability of mining online social networks. Current data mining techniques encounter difficulties of dealing with knowledge interpretation based on complex data sources. The proposed agent-based mining method overcomes network analysis difficulties, while enhancing the knowledge discovery capability through its autonomy and collective intelligence.