The2009InternationalWorkshoponAgentsandDataMiningInteraction(ADMI 2009) was a joint event with AAMAS2009. In recentyears,agents and data mining interaction (ADMI), or agent mining forshort,hasemergedasaverypromisingresearch?eld. Followingthesuccessof ADMI 2006 in Hong Kong, ADMI 2007 in San Jose, and ADMI 2008 in Sydney, the ADMI 2009 workshop in Budapest provided a premier forum for sharing research and engineering results, as well as potential challenges and prospects encountered in the synergy between agents and data mining. As usual, the ADMI workshop encouraged and promoted theoretical and applied research and development, which aims at: - Exploitingagent-drivendatamininganddemonstratinghowintelligentagent technology can contribute to critical data mining problems in theory and practice - Improving data mining-driven agents and showing how data mining can strengthen agent intelligence in research and practical applications - Exploring the integration of agents and data mining toward a super-intelligent information processing and systems - Identifying challenges and directions for future research on the synergy between agents and data mining ADMI 2009 featured two invited talks and twelve selected papers. The ?rst invited talk was on "Agents and Data Mining in Bioinformatics," with the s- ond focusing on "Knowledge-Based Reinforcement Learning. " The ten accepted papers are from seven countries. A majority of submissions came from Eu- pean countries, indicating the boom of ADMI research in Europe. In addition the two invited papers, addressed fundamental issues related to agent-driven data mining, data mining-driven agents, and agent mining applications. The proceedings of the ADMI workshops will be published as part of the LNAIseriesbySpringer.WeappreciatethesupportofSpringer,andinparticular Alfred Hofmann.
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ISBN-13
978-3-642-03603-3 (9783642036033)
DOI
10.1007/978-3-642-03603-3
Schweitzer Klassifikation
Invited Talks and Papers.- Agents and Data Mining in Bioinformatics: Joining Data Gathering and Automatic Annotation with Classification and Distributed Clustering.- Knowledge-Based Reinforcement Learning for Data Mining.- Ubiquitous Intelligence in Agent Mining.- Agents Based Data Mining and Decision Support System.- Agent-Driven Data Mining.- Agent-Enriched Data Mining Using an Extendable Framework.- Auto-Clustering Using Particle Swarm Optimization and Bacterial Foraging.- A Self-Organized Multiagent System for Intrusion Detection.- Towards Cooperative Predictive Data Mining in Competitive Environments.- Data Mining Driven Agents.- Improving Agent Bidding in Power Stock Markets through a Data Mining Enhanced Agent Platform.- Enhancing Agent Intelligence through Data Mining: A Power Plant Case Study.- A Sequence Mining Method to Predict the Bidding Strategy of Trading Agents.- Agent Mining Applications.- Agent Assignment for Process Management: Pattern Based Agent Performance Evaluation.- Concept Learning for Achieving Personalized Ontologies: An Active Learning Approach.- The Complex Dynamics of Sponsored Search Markets.
