|Title||Classification of Melanomas in situ using Knowledge Discovery with Explained CBR|
|Publication Type||Journal Article|
|Year of Publication||2011|
|Journal||Artificial Intelligence in Medicine|
|Keywords||application, CBR, classification, Clustering, knowledge discovery|
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.
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