<?xml version="1.0" encoding="UTF-8"?>
<XML><RECORDS>
<RECORD>
	<REFERENCE_TYPE>0</REFERENCE_TYPE>
	<AUTHORS>
		<AUTHOR>Eva Armengol</AUTHOR>
	</AUTHORS>
	<YEAR>2011</YEAR>
	<TITLE>Classification of Melanomas in situ using Knowledge Discovery with Explained CBR</TITLE>
	<SECONDARY_TITLE>Artificial Intelligence in Medicine</SECONDARY_TITLE>
	<VOLUME>51</VOLUME>
	<PAGES>12</PAGES>
	<DATE>01/2011</DATE>
	<KEYWORDS>
		<KEYWORD>knowledge</KEYWORD>
		<KEYWORD>discovery,</KEYWORD>
		<KEYWORD>classification,</KEYWORD>
		<KEYWORD>clustering,</KEYWORD>
		<KEYWORD>CBR,</KEYWORD>
		<KEYWORD>application</KEYWORD>
	</KEYWORDS>
	<ABSTRACT>The goal of Knowledge Discovery is to extract knowledge from a set of data. Most common techniques used in knowledge discovery are based on clustering methods whose goal is to analyze a set of objects and to obtain clusters based on the similarity between these objects. A desirable characteristic of clustering results is that they should be easily understandable by domain experts. In this paper we introduce LazyCL, a procedure using a lazy learning method to produce explanations on clusters of unlabeled cases. These explanations are the basis on which experts can perform knowledge discovery. Here we use LazyCL to generate a domain theory for classification of melanomas in situ.</ABSTRACT>
</RECORD>
</RECORDS></XML>