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Agent-mining interaction: an emerging area

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TLDR
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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Book

Domain Driven Data Mining

TL;DR: Domain Driven Data Mining enhances the actionability and wider deployment of existing data-centered data mining through a combination of domain and business oriented factors, constraints and intelligence.
Journal ArticleDOI

Actionable knowledge discovery and delivery

TL;DR: Thorough and innovative retrospection and thinking are timely in bridging the gaps and promoting data mining toward next‐generation research and development: namely, the paradigm shift from knowledge discovery from data to actionable knowledge discovery and delivery.
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A multi-disciplinary review of knowledge acquisition methods

TL;DR: It is shown that the KA field is increasingly active due to the higher and higher pace of change in human activity, and the emergence of a fourth category of knowledge acquisition methods, which are based on red-teaming and co-evolution are discussed.
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Automatic Web Data Extraction Based on Genetic Algorithms and Regular Expressions

TL;DR: This chapter proposes an Evolutionary Computation approach to the problem of automatically learn software entities based on Genetic Algorithms and regular expressions, also called wrappers, that will be able to extract some kind of Web data structures from examples.
References
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Book

Agent-Based Hybrid Intelligent Systems: An Agent-Based Framework for Complex Problem Solving

Zili Zhang, +1 more
TL;DR: The author concludes that the agent-based hybrid intelligent system for financial investment planning and the methodology and framework for agent-oriented methodologies and application systems developed in this paper are suitable for use in the real-time environment.
Book

Agent Intelligence Through Data Mining

TL;DR: This paper presents a meta-modelling framework that automates the very labor-intensive and therefore time-heavy and therefore expensive and expensive process of manually mining data for information about agents and their behaviour.
MonographDOI

Intelligent Agents for Data Mining and Information Retrieval

TL;DR: This work discusses the foundation as well as the practical side of intelligent agents and their theory and applications for web data mining and information retrieval.
Proceedings ArticleDOI

Multi-agent technology for distributed data mining and classification

TL;DR: The paper presents the developed and implemented distributed data mining technology, architecture of the multi- agent software tool supporting this technology and demonstrates the key protocols used by agents in collaborative design of an applied multi-agent distributed datamining system.
Journal ArticleDOI

Smart shopper: an agent-based web-mining approach to Internet shopping

TL;DR: A fuzzy neural network is proposed to tackle the uncertainties in practical shopping activities, such as consumer preferences, product specification, product selection, price negotiation, purchase, delivery, after-sales service and evaluation, and the feasibility of the proposed approach for Web-based business transactions is demonstrated.
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