An investigation into the issues of multi-agent data mining

Albashiri, KA, Coenen, F ORCID: 0000-0003-1026-6649 and Leng, P
(2012) An investigation into the issues of multi-agent data mining. Doctor of Philosophy thesis, University of Liverpool.

[img] Text
Thesis.pdf - Author Accepted Manuscript

Download (1MB)
[img] Text
AlbashiriKam_Feb2010_1275.pdf - Author Accepted Manuscript

Download (1MB)


Multi-agent systems (MAS) often deal with complex applications that require distributedproblem solving. In many applications the individual and collective behaviourof the agents depends on the observed data from distributed sources. The field of DistributedData Mining (DDM) deals with these challenges in analyzing distributed dataand offers many algorithmic solutions to perform different data analysis and miningoperations in a fundamentally distributed manner that pays careful attention to the resourceconstraints. Since multi-agent systems are often distributed and agents haveproactive and reactive features, combining DM with MAS for data intensive applicationsis therefore appealing.This Chapter discusses a number of research issues concerned with the use ofMulti-Agent Systems for Data Mining (MADM), also known as agent-driven datamining. The Chapter also examines the issues affecting the design and implementationof a generic and extendible agent-based data mining framework. An ExtendibleMulti-Agent Data mining System (EMADS) Framework for integrating distributeddata sources is presented. This framework achieves high-availability and highperformance without compromising the data integrity and security. © 2010 Nova Science Publishers, Inc. All rights reserved.

Item Type: Thesis (Doctor of Philosophy)
Additional Information: Date: 2010-02 (completed)
Subjects: ?? QA75 ??
Divisions: Faculty of Science and Engineering > School of Electrical Engineering, Electronics and Computer Science
Depositing User: Symplectic Admin
Date Deposited: 11 Jan 2011 12:06
Last Modified: 16 Dec 2022 04:33
DOI: 10.17638/00001275
  • Coenen, Frans