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<title>UTas ePrints - An Incremental Learning Method for Data Mining from Large Databases</title>
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<meta content="Ling, Tristan Ronald" name="eprints.creators_name" />
<meta content="trling@postoffice.utas.edu.au" name="eprints.creators_id" />
<meta content="thesis" name="eprints.type" />
<meta content="2007-02-22" name="eprints.datestamp" />
<meta content="2008-01-08 15:30:00" name="eprints.lastmod" />
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<meta content="An Incremental Learning Method for Data Mining from Large Databases" name="eprints.title" />
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<meta content="280199" name="eprints.subjects" />
<meta content="280213" name="eprints.subjects" />
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<meta content="data mining, incremental learning, artificial intelligence, large databases" name="eprints.keywords" />
<meta content="Knowledge Discovery techniques seek to find new information about a domain
through a combination of existing domain knowledge and data examples from the
domain. These techniques can either be manually performed by an expert, or
automated using software algorithms (Machine Learning). However some domains,
such as the clinical field of Lung Function testing, contain volumes of data too vast
and detailed for manual analysis to be effective, and existing knowledge too complex
for Machine Learning algorithms to be able to adequately discover relevant
knowledge. In many cases this data is also unclassified, with no previous analysis
having been performed. A better approach for these domains might be to involve a
human expert, taking advantage of their expertise to guide the process, and to use
Machine Learning techniques to assist the expert in discovering new and meaningful
relationships in the data. It is hypothesised that Knowledge Acquisition methods
would provide a strong basis for such a Knowledge Discovery method, particularly
methods which can provide incremental verification and validation of knowledge as
it is obtained. This study examines how the MCRDR (Multiple Classification Ripple-
Down Rules) Knowledge Acquisition process can be adapted to develop a new
Knowledge Discovery method, Exposed MCRDR, and tests this method in the
domain of Lung Function. Preliminary results suggest that the EMCRDR method can
be successfully applied to discover new knowledge in a complex domain, and reveal
many potential areas of study and development for the MCRDR method." name="eprints.abstract" />
<meta content="2006-11" name="eprints.date" />
<meta content="published" name="eprints.date_type" />
<meta content="89" name="eprints.pages" />
<meta content="University of Tasmania" name="eprints.institution" />
<meta content="School of Computing" name="eprints.department" />
<meta content="honours" name="eprints.thesis_type" />
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<meta content="Ling, Tristan Ronald (2006) An Incremental Learning Method for Data Mining from Large Databases. Honours thesis, University of Tasmania." name="eprints.citation" />
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<meta content="Knowledge Discovery techniques seek to find new information about a domain
through a combination of existing domain knowledge and data examples from the
domain. These techniques can either be manually performed by an expert, or
automated using software algorithms (Machine Learning). However some domains,
such as the clinical field of Lung Function testing, contain volumes of data too vast
and detailed for manual analysis to be effective, and existing knowledge too complex
for Machine Learning algorithms to be able to adequately discover relevant
knowledge. In many cases this data is also unclassified, with no previous analysis
having been performed. A better approach for these domains might be to involve a
human expert, taking advantage of their expertise to guide the process, and to use
Machine Learning techniques to assist the expert in discovering new and meaningful
relationships in the data. It is hypothesised that Knowledge Acquisition methods
would provide a strong basis for such a Knowledge Discovery method, particularly
methods which can provide incremental verification and validation of knowledge as
it is obtained. This study examines how the MCRDR (Multiple Classification Ripple-
Down Rules) Knowledge Acquisition process can be adapted to develop a new
Knowledge Discovery method, Exposed MCRDR, and tests this method in the
domain of Lung Function. Preliminary results suggest that the EMCRDR method can
be successfully applied to discover new knowledge in a complex domain, and reveal
many potential areas of study and development for the MCRDR method." name="DC.description" />
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<h1 class="ep_tm_pagetitle">An Incremental Learning Method for Data Mining from Large Databases</h1>
<p style="margin-bottom: 1em" class="not_ep_block"><span class="person_name">Ling, Tristan Ronald</span> (2006) <xhtml:em>An Incremental Learning Method for Data Mining from Large Databases.</xhtml:em> Honours thesis, University of Tasmania.</p><p style="margin-bottom: 1em" class="not_ep_block"></p><table style="margin-bottom: 1em" class="not_ep_block"><tr><td valign="top" style="text-align:center"><a onmouseover="EPJS_ShowPreview( event, 'doc_preview_798' );" href="http://eprints.utas.edu.au/793/1/trling_Honours_Thesis.pdf" onmouseout="EPJS_HidePreview( event, 'doc_preview_798' );"><img alt="[img]" src="http://eprints.utas.edu.au/style/images/fileicons/application_pdf.png" class="ep_doc_icon" border="0" /></a><div class="ep_preview" id="doc_preview_798"><table><tr><td><img alt="" src="http://eprints.utas.edu.au/793/thumbnails/1/preview.png" class="ep_preview_image" border="0" /><div class="ep_preview_title">Preview</div></td></tr></table></div></td><td valign="top"><a href="http://eprints.utas.edu.au/793/1/trling_Honours_Thesis.pdf"><span class="ep_document_citation">PDF (Complete thesis)</span></a> - Requires a PDF viewer<br />555Kb</td></tr></table><div class="not_ep_block"><h2>Abstract</h2><p style="padding-bottom: 16px; text-align: left; margin: 1em auto 0em auto">Knowledge Discovery techniques seek to find new information about a domain
through a combination of existing domain knowledge and data examples from the
domain. These techniques can either be manually performed by an expert, or
automated using software algorithms (Machine Learning). However some domains,
such as the clinical field of Lung Function testing, contain volumes of data too vast
and detailed for manual analysis to be effective, and existing knowledge too complex
for Machine Learning algorithms to be able to adequately discover relevant
knowledge. In many cases this data is also unclassified, with no previous analysis
having been performed. A better approach for these domains might be to involve a
human expert, taking advantage of their expertise to guide the process, and to use
Machine Learning techniques to assist the expert in discovering new and meaningful
relationships in the data. It is hypothesised that Knowledge Acquisition methods
would provide a strong basis for such a Knowledge Discovery method, particularly
methods which can provide incremental verification and validation of knowledge as
it is obtained. This study examines how the MCRDR (Multiple Classification Ripple-
Down Rules) Knowledge Acquisition process can be adapted to develop a new
Knowledge Discovery method, Exposed MCRDR, and tests this method in the
domain of Lung Function. Preliminary results suggest that the EMCRDR method can
be successfully applied to discover new knowledge in a complex domain, and reveal
many potential areas of study and development for the MCRDR method.</p></div><table style="margin-bottom: 1em" cellpadding="3" class="not_ep_block" border="0"><tr><th valign="top" class="ep_row">Item Type:</th><td valign="top" class="ep_row">Thesis (Honours)</td></tr><tr><th valign="top" class="ep_row">Keywords:</th><td valign="top" class="ep_row">data mining, incremental learning, artificial intelligence, large databases</td></tr><tr><th valign="top" class="ep_row">Subjects:</th><td valign="top" class="ep_row"><a href="http://eprints.utas.edu.au/view/subjects/280199.html">280000 Information, Computing and Communication Sciences > 280100 Information Systems > 280199 Information Systems not elsewhere classified</a><br /><a href="http://eprints.utas.edu.au/view/subjects/280213.html">280000 Information, Computing and Communication Sciences > 280200 Artificial Intelligence and Signal and Image Processing > 280213 Other Artificial Intelligence</a><br /><a href="http://eprints.utas.edu.au/view/subjects/280205.html">280000 Information, Computing and Communication Sciences > 280200 Artificial Intelligence and Signal and Image Processing > 280205 Text Processing</a></td></tr><tr><th valign="top" class="ep_row">ID Code:</th><td valign="top" class="ep_row">793</td></tr><tr><th valign="top" class="ep_row">Deposited By:</th><td valign="top" class="ep_row"><span class="ep_name_citation"><span class="person_name">Prof Arthur Sale</span></span></td></tr><tr><th valign="top" class="ep_row">Deposited On:</th><td valign="top" class="ep_row">22 Feb 2007</td></tr><tr><th valign="top" class="ep_row">Last Modified:</th><td valign="top" class="ep_row">09 Jan 2008 02:30</td></tr><tr><th valign="top" class="ep_row">ePrint Statistics:</th><td valign="top" class="ep_row"><a target="ePrintStats" href="/es/index.php?action=show_detail_eprint;id=793;">View statistics for this ePrint</a></td></tr></table><p align="right">Repository Staff Only: <a href="http://eprints.utas.edu.au/cgi/users/home?screen=EPrint::View&eprintid=793">item control page</a></p>
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