Data Mining and Multi-agent Integration (eBook, PDF)

Redaktion: Cao, Longbing

• Format: PDF

Produktbeschreibung

Data Mining and Multi-agent Integration presents cutting-edge research, applications and solutions in data mining, and the practical use of innovative information technologies written by leading international researchers in the field. Topics examined include:

• Integration of multiagent applications and data mining



• Mining temporal patterns to improve agents behavior



• Information enrichment through recommendation sharing



• Automatic web data extraction based on genetic algorithms and regular expressions



• A multiagent learning paradigm for medical data mining diagnostic workbench



• A multiagent data mining framework



• Streaming data in complex uncertain environments



• Large data clustering



• A multiagent, multi-objective clustering algorithm



• Interactive web environment for psychometric diagnostics



• Anomalies detection on distributed firewalls using data mining techniques



• Automated reasoning for distributed and multiple source of data



• Video contents identification

Data Mining and Multi-agent Integration is intended for students, researchers, engineers and practitioners in the field, interested in the synergy between agents and data mining. This book is also relevant for readers in related areas such as machine learning, artificial intelligence, intelligent systems, knowledge engineering, human-computer interaction, intelligent information processing, decision support systems, knowledge management, organizational computing, social computing, complex systems, and soft computing.

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Produktdetails

• Verlag: Springer New York
• Seitenzahl: 334
• Erscheinungstermin: 25. Juli 2009
• Englisch
• ISBN-13: 9781441905222
• Artikelnr.: 37341276

Inhaltsangabe

to Agents and Data Mining Interaction.- to Agent Mining Interaction and Integration.- Towards the Integration of Multiagent Applications and Data Mining.- Agent-Based Distributed Data Mining: A Survey.- Data Mining Driven Agents.- Exploiting Swarm Behaviour of Simple Agents for Clustering Web Users' Session Data.- Mining Temporal Patterns to Improve Agents Behavior: Two Case Studies.- A Multi-Agent System for Extracting and Analysing Users' Interaction in a Collaborative Knowledge Management System.- Towards Information Enrichment through Recommendation Sharing.- A Multiagent-based Intrusion Detection System with the Support of Multi-Class Supervised Classification.- Automatic Web Data Extraction Based on Genetic Algorithms and Regular Expressions.- Establishment and Maintenance of a Knowledge Network by Means of Agents and Implicit Data.- Equipping Intelligent Agents with Commonsense Knowledge acquired from Search Query Logs: Results from an Exploratory Story.- A Multi-Agent Learning Paradigm for Medical Data Mining Diagnostic Workbench.- Agent Driven Data Mining.- The EMADS Extendible Multi-Agent Data Mining Framework.- A Multiagent Approach to Adaptive Continuous Analysis of Streaming Data in Complex Uncertain Environments.- Multiagent Systems for Large Data Clustering.- A Multiagent, Multiobjective Clustering Algorithm.- Integration of Agents and Data Mining in Interactive Web Environment for Psychometric Diagnostics.- A Multi-Agent Framework for Anomalies Detection on Distributed Firewalls Using Data Mining Techniques.- Competitive-Cooperative Automated Reasoning from Distributed and Multiple Source of Data.- Normative Multi-Agent Enriched Data Mining to Support E-Citizens.- CV-Muzar - The Virtual Community Environment that Uses Multiagent Systems for Formationof Groups.- Agent based Video Contents Identification and Data Mining Using Watermark based Filtering.- Erratum.

Rezensionen

From the reviews: "This book promotes the latest methodological, technical, and practical advancements in the use of agents in data mining applications. ... chapters include extensive bibliographies. ... The book is intended for students, researchers, engineers, and practitioners, in both agent and data mining areas, who are interested in the potential of integrating agents and mining. ... interested readers who are willing to make an effort to build on the book's material will benefit from reading it." (J. P. E. Hodgson, ACM Computing Reviews, December, 2009)