An agent-based hybrid framework for database mining

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dc.contributor.author Zhang, Z
dc.contributor.author Zhang, C
dc.contributor.author Zhang, S
dc.date.accessioned 2009-12-21T02:31:26Z
dc.date.issued 2003-05
dc.identifier.citation Applied Artificial Intelligence, 2003, 17 (5-6), pp. 383 - 398
dc.identifier.issn 0883-9514
dc.identifier.other C1 en_US
dc.identifier.uri http://hdl.handle.net/10453/4041
dc.description.abstract While knowledge discovery in databases (KDD) is defined as an iterative sequence of the following steps: data pre-processing, data mining, and post data mining, a significant amount of research in data mining has been done, resulting in a variety of algorithms and techniques for each step. However, a single data-mining technique has not been proven appropriate for every domain and data set. Instead, several techniques may need to be integrated into hybrid systems and used cooperatively during a particular data-mining operation. That is, hybrid solutions are crucial for the success of data mining. This paper presents a hybrid framework for identifying patterns from databases or multi-databases. The framework integrates these techniques for mining tasks from an agent point of view. Based on the experiments conducted, putting different KDD techniques together into the agent-based architecture enables them to be used cooperatively when needed. The proposed framework provides a highly flexible and robust data-mining platform and the resulting systems demonstrate emergent behaviours although it does not improve the performance of individual KDD techniques.
dc.language eng
dc.title An agent-based hybrid framework for database mining
dc.type Journal Article
dc.parent Applied Artificial Intelligence
dc.journal.volume 5-6
dc.journal.volume 17
dc.journal.number 5-6 en_US
dc.publocation Philadelphia, USA en_US
dc.identifier.startpage 383 en_US
dc.identifier.endpage 398 en_US
dc.cauo.name FEIT.School of Software en_US
dc.conference Verified OK en_US
dc.for 080101 Adaptive Agents and Intelligent Robotics
dc.for 080109 Pattern Recognition and Data Mining
dc.personcode 020030
dc.personcode 011221
dc.percentage 70 en_US
dc.classification.name Pattern Recognition and Data Mining en_US
dc.classification.type FOR-08 en_US
pubs.embargo.period Not known
pubs.organisational-group /University of Technology Sydney
pubs.organisational-group /University of Technology Sydney/Faculty of Engineering and Information Technology
pubs.organisational-group /University of Technology Sydney/Strength - Quantum Computation and Intelligent Systems
utslib.copyright.status Closed Access
utslib.copyright.date 2015-04-15 12:17:09.805752+10
pubs.consider-herdc true
utslib.collection.history Closed (ID: 3)
utslib.collection.history Uncategorised (ID: 363)


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