New PDF release: Data Mining and Multi-agent Integration

By Longbing Cao (auth.), Longbing Cao (eds.)

ISBN-10: 1441905219

ISBN-13: 9781441905215

Data Mining and Multi-agent Integration offers state-of-the-art learn, functions and ideas in facts mining, and the sensible use of leading edge info applied sciences written via best foreign researchers within the box. themes tested include:

  • Integration of multiagent purposes and knowledge mining
  • Mining temporal styles to enhance brokers behavior
  • Information enrichment via advice sharing
  • Automatic internet information extraction in response to genetic algorithms and ordinary expressions
  • A multiagent studying paradigm for clinical facts mining diagnostic workbench
  • A multiagent info mining framework
  • Streaming facts in complicated doubtful environments
  • Large info clustering
  • A multiagent, multi-objective clustering algorithm
  • Interactive net setting for psychometric diagnostics
  • Anomalies detection on allotted firewalls utilizing info mining techniques
  • Automated reasoning for allotted and a number of resource of data
  • Video contents identification

Data Mining and Multi-agent Integration is meant for college kids, researchers, engineers and practitioners within the box, attracted to the synergy among brokers and knowledge mining. This publication can also be suitable for readers in comparable parts comparable to computing device studying, man made intelligence, clever structures, wisdom engineering, human-computer interplay, clever details processing, selection aid structures, wisdom administration, organizational computing, social computing, complicated structures, and smooth computing.

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Additional info for Data Mining and Multi-agent Integration

Example text

Correspondingly, solutions for agent service based application integration, distributed data preparation, distributed agent coordination and parallel agent computing should be considered. In many cases of data mining, people should study algorithms that can adapt to dynamic data changes, dynamic user requests. To this end, it has the potential for agents to detect and reason such changes. Automated and adaptive data mining algorithms should be studied. The following is a list of some research open issues and promising areas.

Automated and adaptive data mining algorithms should be studied. The following is a list of some research open issues and promising areas. - Activity modeling and mining - Agent-based enterprise data mining 22 - Longbing Cao Agent-based data mining infrastructure Agent-based data warehouse Agent-based mining process and project management Agent-based distributed data mining Agent-based distributed learning Agent-based grid computing Agent-based human mining cooperation Agent-based link mining Agent-based multi-data source mining Agent-based interactive data mining Agent-enriched ontology mining Agent-based parallel data mining Agent-based web mining Agent-based text mining Agent-based ubiquitous data mining Agent knowledge management in distributed data mining Agent for data mining data preparation Agent-human-cooperated data mining Agent networks in distributed knowledge discovery and servicing Agent service-based KDD infrastructure Agent-supported domain knowledge involvement in KDD Agent system providing data mining services Automated data mining learning Autonomous learning Distributed agent-based data preprocessing Distributed learning Domain intelligence in agent-based data mining Mobile agent-based knowledge discovery Protocols for agent-based data mining Self-organizing data mining learning.

Usually DM tools are introduced to enterprises as components-off-the-shelf. These tools are used by human experts to examine their corporate or environmental databases to develop strategies and take decisions. This procedure often proves time-consuming and inefficient. By exploiting concurrency and multiple instantiation of agent types (cloning capabilities) of MAS systems, and by applying data mining techniques for embedding intelligent reasoning into them, useful recommendations can be much more quickly diffused while parallelism can be applied to non-related tasks, pushing system performance even higher.

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Data Mining and Multi-agent Integration by Longbing Cao (auth.), Longbing Cao (eds.)

by Kevin

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