Data Mining and Multi-Agent Integration

ISBN-13: 9781441905215 / Angielski / Twarda / 2009 / 334 str.

Cao, Longbing

Opis

This edited book reports on the state-of-the-art research and development progress in promoting the synergism of two cutting-edge technologies - agents and data mining. The volume presents the methodologies, algorithms and systems that integrate these two technologies. The text highlights: basic concepts and techniques in agents - data mining interaction and integration techniques to build data mining systems from agent perspectives, and the advantages of this approach; how to enhance agents capabilities through data mining; efficient and effective ways to integrate agents and data mining techniques; case studies demonstrating the mutual enhancement of the integration; theoretical and applied problems arising at the cross boundary of multiagent, data mining and KDD technologies; new problems, challenges, and their impact on the future trends in these areas; and, information technology as a whole.

Recenzje

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)

Spis treści

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 Formation of Groups.- Agent based Video Contents Identification and Data Mining Using Watermark based Filtering.- Erratum.

Blurb

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.