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Book ChapterDOI

Agent-mining interaction: an emerging area

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This paper draws a high-level overview of the agent-mining interaction from the perspective of an emerging area in the scientific family, and summarizes key driving forces, originality, major research directions and respective topics, and the progression of research groups, publications and activities of agent- mining interaction.
Abstract
In the past twenty years, agents (we mean autonomous agent and multi-agent systems) and data mining (also knowledge discovery) have emerged separately as two of most prominent, dynamic and exciting research areas. In recent years, an increasingly remarkable trend in both areas is the agent-mining interaction and integration. This is driven by not only researcher's interests, but intrinsic challenges and requirements from both sides, as well as benefits and complementarity to both communities through agent-mining interaction. In this paper, we draw a high-level overview of the agent-mining interaction from the perspective of an emerging area in the scientific family. To promote it as a newly emergent scientific field, we summarize key driving forces, originality, major research directions and respective topics, and the progression of research groups, publications and activities of agent-mining interaction. Both theoretical and application-oriented aspects are addressed. The above investigation shows that the agent-mining interaction is attracting everincreasing attention from both agent and data mining communities. Some complicated challenges in either community may be effectively and efficiently tackled through agent-mining interaction. However, as a new open area, there are many issues waiting for research and development from theoretical, technological and practical perspectives.

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Ubiquitous Intelligence in Agent Mining

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Mining frequent agent action patterns for effective multi-agent-based web service composition

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A Multiagent, Multiobjective Clustering Algorithm

TL;DR: This chapter presents MACC, a multi ant colony and multiobjective clustering algorithm that can handle distributed data, a typical necessity in scenarios involving many agents.
Journal ArticleDOI

An Agent-Based Hybrid System for Microarray Data Analysis

TL;DR: It is demonstrated that agent-based approaches are suitable for building hybrid systems in general and that a genetic ensemble system is appropriate for microarray data analysis in particular.
References
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Book

Data Mining: Concepts and Techniques

TL;DR: This book presents dozens of algorithms and implementation examples, all in pseudo-code and suitable for use in real-world, large-scale data mining projects, and provides a comprehensive, practical look at the concepts and techniques you need to get the most out of real business data.
Book

An Introduction to MultiAgent Systems

TL;DR: A multi-agent system is a distributed computing system with autonomous interacting intelligent agents that coordinate their actions so as to achieve its goal(s) jointly or competitively.
Book

Introduction to Multiagent Systems

TL;DR: A multi-agent system (MAS) as discussed by the authors is a distributed computing system with autonomous interacting intelligent agents that coordinate their actions so as to achieve its goal(s) jointly or competitively.
BookDOI

Intelligent Data Engineering and Automated Learning – IDEAL 2006

TL;DR: Learning and Information Processing -- Data Mining, Retrieval and Management -- Bioinformatics and Bio-inspired Models -- Agents and Hybrid Systems -- Financial Engineering -- Special Session on Nature-Inspired Date Technologies.
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