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  5.     <title>UTas ePrints - An Incremental Learning Method for Data Mining from Large Databases</title>
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  13.     <meta content="Ling, Tristan Ronald" name="eprints.creators_name" />
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  26. <meta content="Knowledge Discovery techniques seek to find new information about a domain
  27. through a combination of existing domain knowledge and data examples from the
  28. domain. These techniques can either be manually performed by an expert, or
  29. automated using software algorithms (Machine Learning). However some domains,
  30. such as the clinical field of Lung Function testing, contain volumes of data too vast
  31. and detailed for manual analysis to be effective, and existing knowledge too complex
  32. for Machine Learning algorithms to be able to adequately discover relevant
  33. knowledge. In many cases this data is also unclassified, with no previous analysis
  34. having been performed. A better approach for these domains might be to involve a
  35. human expert, taking advantage of their expertise to guide the process, and to use
  36. Machine Learning techniques to assist the expert in discovering new and meaningful
  37. relationships in the data. It is hypothesised that Knowledge Acquisition methods
  38. would provide a strong basis for such a Knowledge Discovery method, particularly
  39. methods which can provide incremental verification and validation of knowledge as
  40. it is obtained. This study examines how the MCRDR (Multiple Classification Ripple-
  41. Down Rules) Knowledge Acquisition process can be adapted to develop a new
  42. Knowledge Discovery method, Exposed MCRDR, and tests this method in the
  43. domain of Lung Function. Preliminary results suggest that the EMCRDR method can
  44. be successfully applied to discover new knowledge in a complex domain, and reveal
  45. many potential areas of study and development for the MCRDR method." name="eprints.abstract" />
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  52. <meta content="Aamodt, A &amp; Plaza, E 1994, 'Case-Based Reasoning: Foundational Issues, Methodological
  53. Variations, and System Approaches', AI Communications, vol. 7, no. 1, pp. 33-59.
  54. Aaron, S, Dales, R &amp; Cardinal, P 1999, How Accurate Is Spirometry at Predicting Restrictive
  55. Pulmonary Impairment?*.
  56. Abe, H &amp; Yamaguchi, T 2005, 'Implementing an Integrated Time-Series Data Mining
  57. Environment - A Case Study of Medical KDD on Chronic Hepatitis', paper presented
  58. to First International Conference on Complex Medical Engineering (CME2005),
  59. Takamatsu, Kagawa, Japan.
  60. Aha, DW 1991, 'Case-Based Learning Algorithms', paper presented to Proceedings of the
  61. DARPA Case-Based Reasoning Workshop, Washington D.C.
  62. Bachant, J &amp; McDermott, J 1984, 'RI Revisited: Four Years in the Trenches', AI Magazine,
  63. vol. 5, no. 3, pp. 21-32.
  64. Barker, V, O'Connor, D, Bachant, J &amp; Soloway, E 1989, 'Expert systems for configuration at
  65. Digital: XCON and beyond', Communications of the ACM, vol. 32, no. 3, pp. 298-
  66. 318.
  67. Bindoff, IK 2005, 'An Intelligent Decision Support System for Automated Medication Review',
  68. University of Tasmania.
  69. Buchanan, B &amp; Shortliffe, E 1984, Rule-based expert systems: the MYCIN experiments of
  70. the Stanford Heuristic Programming Project, Addison-Wesley.
  71. Buchanan, B, Barstow, D, Bechtal, R, Bennett, J, Clancey, W, Kulikowski, C, Mitchell, T &amp;
  72. Waterman, D 1983, 'Constructing an expert system', Building Expert Systems.
  73. Catlett, J 1992, 'Ripple down rules as a mediating representation in interactive induction',
  74. paper presented to Proceedings of the Second Japanese Knowledge Acquisition for
  75. Knowledge-Based Systems Workshop, Kobe, Japan, Nov 9-13.
  76. Chi, RH &amp; Kiang, MY 1991, 'An integrated approach of rule-based and case-based
  77. reasoning for decision support', in Proceedings of the 19th annual conference on
  78. Computer Science, ACM Press, San Antonio, Texas, United States, pp. 255-67.
  79. Clancey, WJ 1984, 'Knowledge acquisition for classification expert systems', in Proceedings
  80. of the 1984 annual conference of the ACM on The fifth generation challenge, ACM
  81. Press, pp. 11-4.
  82. ---- 1993, 'Situated action: A neuropsychological interpretation (Response to Vera and
  83. Simon)', Cognitive Science, vol. 17, no. 1, pp. 87-116.
  84. Clerkin, P, Cunningham, P &amp; Hayes, C 2001, 'Ontology Discovery for the Semantic Web
  85. Using Hierarchical Clustering', Semantic Web Mining Workshop at ECML/PKDD-
  86. 2001, September, vol. 3.
  87. Compton, P &amp; Jansen, R 1989, 'A philosophical basis for knowledge acquisition', paper
  88. presented to European Knowledge Acquisition for Knowledge-Based Systems,
  89. Paris.
  90. Compton, P &amp; Edwards, G 1994, 'A 2000 Rule Expert System Without a Knowledge
  91. Engineer', paper presented to Proceedings of the 8th AAAI-Sponsored Ban
  92. Knowledge Acquisition for Knowledge-Based Systems Workshop, Banff, Canada.
  93. Compton, P, Cao, T &amp; Kerr, J 2004, 'Generalising Incremental Knowledge Acquisition', paper
  94. presented to Pacific Rim Knowledge Acquisition Workshop, Auckland, 9th - 10th
  95. August.
  96. Compton, P, Kang, B, Preston, P &amp; Mulholland, M 1993, 'Knowledge Acquisition without
  97. Analysis', paper presented to Knowledge Acquisition for Knowledge-Based Systems,
  98. Springer Verlag.
  99. Compton, P, Edwards, G, Kang, B, Lazarus, L, Malor, R, Preston, P &amp; Srinivasan, A 1992,
  100. 'Ripple down rules: Turning knowledge acquisition into knowledge maintenance.'
  101. Artificial Intelligence in Medicine, vol. 4, no. 6, pp. 463-75.
  102. D P Johns, P, FANZSRS 2006, to T Ling, July 2006.
  103. Dazeley, R &amp; Kang, B 2004, 'An Online Classification and Prediction Hybrid System for
  104. Knowledge Discovery in Databases', paper presented to The 2nd International
  105. Conference on Artificial Intelligence in Science and Technology, Hobart, Australia.
  106. 72
  107. Edwards, G, Compton, P, Malor, R, Srinivasan, A &amp; Lazarus, L 1993, 'PEIRS: a pathologistmaintained
  108. expert system for the interpretation of chemical pathology reports',
  109. Pathology, no. 25, pp. 27-34.
  110. Feigenbaum, EA 1977, The art of artificial intelligence: I. Themes and case studies of
  111. knowledge engineering, Stanford University.
  112. Féret, M &amp; Glasgow, J 1997, 'Combining Case-Based and Model-Based Reasoning for the
  113. Diagnosis of Complex Devices', Applied Intelligence, vol. 7, no. 1, pp. 57-78.
  114. Ferguson, G, Enright, P, Buist, A &amp; Higgins, M 2000, Office Spirometry for Lung Health
  115. Assessment in Adults* A Consensus Statement From the National Lung Health
  116. Education Program.
  117. Frawley, W, Piatetsky-Shapiro, G &amp; Matheus, C 1992, 'Knowledge Discovery in Databases:
  118. An Overview', AI Magazine, vol. 13, no. 3, pp. 57-70.
  119. Gaines, B &amp; Boose, J 1988, Knowledge Acquisition for Knowledge-Based Systems, vol. 1,
  120. Academic Press, Inc., Orlando, FL, USA.
  121. Gaines, B &amp; Compton, P 1992, 'Induction of Ripple Down Rules', paper presented to
  122. Proceedings of the 5th Australian Conference on Artical Intelligence, Hobart,
  123. Australia.
  124. Gaines, BR 1993, 'Modeling and Extending Expertise', in Proceedings of the 7th European
  125. Workshop on Knowledge Acquisition for Knowledge-Based Systems, Springer-
  126. Verlag, pp. 1-22.
  127. Gerber, A, Glynn, E, MacDonald, A, Lawley, M &amp; Raymond, K 2004, 'Modelling for
  128. Knowledge Discovery', paper presented to Workshop on Model-driven Evolution of
  129. Legacy Systems (MELS).
  130. Glady, C, Aaron, S, Lunau, M, Clinch, J &amp; Dales, R 2003, A Spirometry-Based Algorithm To
  131. Direct Lung Function Testing in the Pulmonary Function Laboratory*.
  132. Goebel, M &amp; Gruenwald, L 1999, 'A survey of data mining and knowledge discovery software
  133. tools', SIGKDD Explor. Newsl., vol. 1, no. 1, pp. 20-33.
  134. Goldberg, DE &amp; Holland, JH 1988, 'Genetic Algorithms and Machine Learning', Machine
  135. Learning, vol. V3, no. 2, pp. 95-9.
  136. Golding, A &amp; Rosenbloom, P 1996, 'Improving accuracy by combining rule-based and casebased
  137. reasoning', Artificial Intelligence, vol. 87, no. 1, pp. 215-54.
  138. Grefenstette, J, Ramsey, C &amp; Schultz, A 1990, 'Learning sequential decision rules using
  139. simulation models and competition', Machine Learning, vol. 5, no. 4, pp. 355-81.
  140. Hall, M &amp; Smith, L 1998, 'Practical feature subset selection for machine learning', paper
  141. presented to Proceedings of the 21st Australasian Computer Science Conference.
  142. Hand, D &amp; Vinciotti, V 2003, 'Choosing k for two-class nearest neighbour classifiers with
  143. unbalanced classes', Pattern Recognition Letters, vol. 24, no. 9-10, pp. 1555-62.
  144. Hong, T-P, Wang, T-T, Wang, S-L &amp; Chien, B-C 2000, 'Learning a coverage set of maximally
  145. general fuzzy rules by rough sets', Expert Systems with Applications, vol. 19, no. 2,
  146. pp. 97-103.
  147. Hughes, D &amp; Empey, D 1981, Lung Function for the Clinician, Academic Press Grune and
  148. Stratton, London.
  149. Ihrig, L &amp; Kambhampati, S 1995, 'An Explanation-Based Approach to Improve Retrieval in
  150. Case-Based Planning', paper presented to European Workshop on Planning
  151. (EWSP-95).
  152. Kang, B &amp; Compton, P 1992, 'Knowledge Acquisition in Context: the Multiple Classification
  153. Problem', paper presented to Proceedings of the Pacific Rim International
  154. Conference on Artificial Intelligence, Seoul.
  155. Kang, B, Compton, P &amp; Preston, P 1995, 'Multiple Classification Ripple Down Rules:
  156. Evaluation and Possibilities', paper presented to Proceedings 9th Banff Knowledge
  157. Acquisition for Knowledge Based Systems Workshop, Banff, Feb 26 - March 3.
  158. ---- 1998, 'Simulated Expert Evaluation of Multiple Classification Ripple Down Rules', paper
  159. presented to 11th Banff knowledge acqusition for knowledge-based systems
  160. workshop, University of Calgary.
  161. Kang, B, Yoshida, K, Motoda, H &amp; Compton, P 1997, 'Help desk system with intelligent
  162. interface', Applied Artificial Intelligence, vol. 11, no. 7-8, pp. 611-31.
  163. Kolodner, J 1991, 'Improving Human Decision Making through Case-Based Decision Aiding',
  164. AI Magazine, vol. 12, no. 2, pp. 52-68.
  165. 73
  166. Kowalski, A 1991, 'Case-based reasoning and the deep structure approach to knowledge
  167. representation', in Proceedings of the 3rd international conference on Artificial
  168. intelligence and law, ACM Press, Oxford, England, pp. 21-30.
  169. Kurniawati, R, Jin, J &amp; Shepherd, J 1998, 'Efficient nearest-neighbour searches using
  170. weighted euclidean metrics', Proceedings of the 16th British National Conferenc on
  171. Databases: Advances in Databases, pp. 64-76.
  172. Laszlo, G 1994, Pulmonary FunctionA Guide for Clinicians, Cambridge University Press,
  173. Cambridge.
  174. Leondes, C 2002, Expert Systems, vol. 4, 6 vols., Academic Press, San Diego.
  175. Liou, YI 1990, 'Knowledge acquisition: issues, techniques, and methodology', in Proceedings
  176. of the 1990 ACM SIGBDP conference on Trends and directions in expert systems,
  177. ACM Press, Orlando, Florida, United States, pp. 212-36.
  178. Manago, M, Althoff, K, Auriol, E, Traphoner, R, Stefan, W, Conruyt, N &amp; Maurer, F 1993,
  179. 'Induction and Reasoning from Cases', paper presented to First European Workshop
  180. on Case-Based Reasoning, Kaiserslautern, Germany.
  181. MedGraphics Pulmonary Consult™ Software, 2006, Medical Graphics Corporation, viewed
  182. 10/13/2006 2006, <http://www.medgraphics.com/datasheet_pconsult.html>.
  183. Miller, A 1987, Pulmonary Function Tests, Grune and Stratton, Orlando.
  184. Mitchell, T 1997, 'Artificial Neural Networks', in T Mitchell (ed.), Machine Learning, McGraw-
  185. Hill, pp. 82-112.
  186. Oswald, H, Phelan, P, Lanigan, A, Hibbert, M, Carlin, J, Bowes, G &amp; Olinsky, A 1997,
  187. 'Childhood asthma and lung function in mid-adult life.' Pediatr Pulmonol, vol. 23, no.
  188. 1, pp. 14-20.
  189. Paris, C &amp; Gil, Y 1993, 'EXPECT: Intelligent Support for Knowledge Base Refinement', in
  190. Proceedings of the 7th European Workshop on Knowledge Acquisition for
  191. Knowledge-Based Systems, Springer-Verlag, pp. 220-36.
  192. Pribor, H 1989, 'Expert systems in laboratory medicine: A practical consultative application',
  193. Journal of Medical Systems, vol. 13, no. 2, pp. 103-9.
  194. Punjabi, N 1998, 'Correction of single-breath helium lung volumes in patients with airflow
  195. obstruction', Chest, vol. 114, no. 3, pp. 907-18.
  196. Quinlan, J 1986, 'Induction of decision trees', Machine Learning, vol. 1, no. 1, pp. 81-106.
  197. ---- 1993, C4.5: Programs for Machine Learning, vol. 1, 1 vols., Morgan Kaufmann
  198. Publishers Inc., Sydney.
  199. ---- 1996, 'Bagging, boosting, and C4. 5', paper presented to Proceedings of the Thirteenth
  200. National Conference on Artificial Intelligence.
  201. Quinlan, JR 1987, 'Simplifying decision trees', Int. J. Man-Mach. Stud., vol. 27, no. 3, pp.
  202. 221-34.
  203. Richards, D &amp; Compton, P 1997, 'Combining Formal Concept Analysis and Ripple Down
  204. Rules to Support the Reuse of Knowledge', paper presented to Proceedings
  205. Software Engineering Knowledge Engineering SEKE’97, Madrid, June 18-20.
  206. Richards, D &amp; Busch, P 2003, 'Acquiring and Applying Contextualised Tacit Knowledge',
  207. JOURNAL OF INFORMATION AND KNOWLEDGE MANAGEMENT, vol. 2, pp. 179-
  208. 90.
  209. Richards, D, Chellen, V &amp; Compton, P 1996, 'The Reuse of Ripple Down Rule Knowledge
  210. Bases: Using Machine Learning to Remove Repetition', paper presented to
  211. Proceedings of Pacific Knowledge Acquisition Workshop PKAW’96.
  212. Roberto J. Bayardo, J &amp; Agrawal, R 1999, 'Mining the most interesting rules', in Proceedings
  213. of the fifth ACM SIGKDD international conference on Knowledge discovery and data
  214. mining, ACM Press, San Diego, California, United States, pp. 145-54.
  215. Ruppel, GL 1994, Manual of Pulmonary Function Testing, 7 edn, Mosby, St Louis.
  216. Sester, M 2000, 'Knowledge acquisition for the automatic interpretation of spatial data',
  217. International Journal of Geographical Information Science, vol. 14, no. 1, pp. 1-24.
  218. Shaheen, S, Sterne, J, Tucker, J &amp; Florey, C 1998, Birth weight, childhood lower respiratory
  219. tract infection, and adult lung function, British Thoracic Society.
  220. Singh, PK 2006, 'Knowledge-based Annotation of Medical Images', The University of New
  221. South Wales.
  222. Snow, M, Fallat, R, Tyler, W &amp; Hsu, S 1988, 'Pulmonary Consult: Concept to application of
  223. an expert system', Journal of Clinical Engineering, vol. 13, no. 3.
  224. 74
  225. Srinivasan, A, Compton, P, Malor, R, Edwards, G &amp; Lazarus, L 1992, 'Knowledge Acquisition
  226. in Context for a Complex Domain', paper presented to Proceedings of the Fifth
  227. European Knowledge Acquisition Workshop.
  228. Swanney, M, Beckert, L, Frampton, C, Wallace, L, Jensen, R &amp; Crapo, R 2004, Validity of
  229. the American Thoracic Society and Other Spirometric Algorithms Using FVC and
  230. Forced Expiratory Volume at 6 s for Predicting a Reduced Total Lung Capacity*.
  231. Towell, G &amp; Shavlik, J 1994, 'Knowledge-based artificial neural networks', Artificial
  232. Intelligence, vol. 70, no. 1, pp. 119-65.
  233. Towell, GG &amp; Shavlik, JW 1993, 'Extracting refined rules from knowledge-based neural
  234. networks', Machine Learning, vol. V13, no. 1, pp. 71-101.
  235. Tsumoto, S 1998, 'Modelling medical diagnostic rules based on rough sets', paper presented
  236. to Proceedings of the First International Conference on Rough Sets and Current
  237. Trends in Computing.
  238. ---- 2000, 'Automated discovery of positive and negative knowledge in clinical databases',
  239. Engineering in Medicine and Biology Magazine, IEEE, vol. 19, no. 4, pp. 56-62.
  240. ---- 2004, 'Mining diagnostic rules from clinical databases using rough sets and medical
  241. diagnostic model', Information Sciences, vol. 162, no. 2, pp. 65-80.
  242. Watson, I &amp; Marir, F 1994, 'Case-Based Reasoning: A Review', The Knowledge Engineering
  243. Review, vol. 9, no. 4, pp. 327-54.
  244. Witten, IH &amp; Frank, E 2000, Data Mining: Practical Machine Learning Tools with Java
  245. Implementations, Morgan Kaufmann Publishers, San Diego.
  246. Yamaguti, T &amp; Kurematsu, M 1993, 'Legal knowledge acquisition using case-based
  247. reasoning and model inference', in Proceedings of the 4th international conference
  248. on Artificial intelligence and law, ACM Press, Amsterdam, The Netherlands, pp. 212-
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  258. <meta content="Knowledge Discovery techniques seek to find new information about a domain
  259. through a combination of existing domain knowledge and data examples from the
  260. domain. These techniques can either be manually performed by an expert, or
  261. automated using software algorithms (Machine Learning). However some domains,
  262. such as the clinical field of Lung Function testing, contain volumes of data too vast
  263. and detailed for manual analysis to be effective, and existing knowledge too complex
  264. for Machine Learning algorithms to be able to adequately discover relevant
  265. knowledge. In many cases this data is also unclassified, with no previous analysis
  266. having been performed. A better approach for these domains might be to involve a
  267. human expert, taking advantage of their expertise to guide the process, and to use
  268. Machine Learning techniques to assist the expert in discovering new and meaningful
  269. relationships in the data. It is hypothesised that Knowledge Acquisition methods
  270. would provide a strong basis for such a Knowledge Discovery method, particularly
  271. methods which can provide incremental verification and validation of knowledge as
  272. it is obtained. This study examines how the MCRDR (Multiple Classification Ripple-
  273. Down Rules) Knowledge Acquisition process can be adapted to develop a new
  274. Knowledge Discovery method, Exposed MCRDR, and tests this method in the
  275. domain of Lung Function. Preliminary results suggest that the EMCRDR method can
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  390.     <h1 class="ep_tm_pagetitle">An Incremental Learning Method for Data Mining from Large Databases</h1>
  391.     <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
  392. through a combination of existing domain knowledge and data examples from the
  393. domain. These techniques can either be manually performed by an expert, or
  394. automated using software algorithms (Machine Learning). However some domains,
  395. such as the clinical field of Lung Function testing, contain volumes of data too vast
  396. and detailed for manual analysis to be effective, and existing knowledge too complex
  397. for Machine Learning algorithms to be able to adequately discover relevant
  398. knowledge. In many cases this data is also unclassified, with no previous analysis
  399. having been performed. A better approach for these domains might be to involve a
  400. human expert, taking advantage of their expertise to guide the process, and to use
  401. Machine Learning techniques to assist the expert in discovering new and meaningful
  402. relationships in the data. It is hypothesised that Knowledge Acquisition methods
  403. would provide a strong basis for such a Knowledge Discovery method, particularly
  404. methods which can provide incremental verification and validation of knowledge as
  405. it is obtained. This study examines how the MCRDR (Multiple Classification Ripple-
  406. Down Rules) Knowledge Acquisition process can be adapted to develop a new
  407. Knowledge Discovery method, Exposed MCRDR, and tests this method in the
  408. domain of Lung Function. Preliminary results suggest that the EMCRDR method can
  409. be successfully applied to discover new knowledge in a complex domain, and reveal
  410. 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 &gt; 280100 Information Systems &gt; 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 &gt; 280200 Artificial Intelligence and Signal and Image Processing &gt; 280213 Other Artificial Intelligence</a><br /><a href="http://eprints.utas.edu.au/view/subjects/280205.html">280000 Information, Computing and Communication Sciences &gt; 280200 Artificial Intelligence and Signal and Image Processing &gt; 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&amp;eprintid=793">item control page</a></p>
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