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

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

Data Mining and Multi-agent Integration provides state of the art examine, functions and strategies in information mining, and the sensible use of cutting edge info applied sciences written by way of best foreign researchers within the box. issues tested include:

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

Data Mining and Multi-agent Integration is meant for college students, researchers, engineers and practitioners within the box, attracted to the synergy among brokers and knowledge mining. This publication can be appropriate for readers in similar components reminiscent of desktop studying, man made intelligence, clever structures, wisdom engineering, human-computer interplay, clever info processing, selection help platforms, wisdom administration, organizational computing, social computing, advanced platforms, and gentle computing.

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Extra info for Data Mining and Multi-agent Integration

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Knowledge discovered in data and then fed into agents can greatly enhance the self-organization and learning performance of agents. • DM can greatly enhance the learning and knowledge processing capability of agents, through involving DM algorithms in the building-blocks of agent learning systems. As a result, agents can learn from the data and from the environment before planning and reasoning are made. • DM can enhance the agent capability of handling uncertainty through historical event analysis and dynamic mining and active learning; through mining agent behavioral data, it is possible to reach a balance and trade-off between agent autonomy and supervised evolution; the outcomes of self-organization and emergence become much more certain, controllable and predictable.

There are often gaps between technical outcomes and business expectations in developing workable agents and data mining algorithms and systems, which is due to the inconsistency and incompleteness of evaluation systems between technical and business concerns. As a result, the resulting deliverables are often not of interest to business people and are not operable for action-taking in business problemsolving. An ideal scenario is to generate algorithms and systems that care about concerns from both the technical and business aspects and from both objective and subjective perspectives.

1 Introduction to Agent Mining Interaction and Integration 23 This windfall of information often requires another level of distillation to elicit the knowledge that is hidden in voluminous data repositories. Data mining can be used to extract knowledge nuggets that will constitute the building blocks of agent intelligence. Here, intelligence is defined loosely so as to encompass a wide range of implementations from fully deterministic decision trees to self-organizing communities of autonomous agents.

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