ADBIS'07 Proceedings of the 11th East European conference on Advances in databases and information systems
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One of the problems with existing clustering methods isthat the interpretation of clusters may be difficult. Two differentapproaches have been used to solve this problem:conceptual clustering in machine learning and clusteringvisualization in statistics and graphics. The purpose of thispaper is to investigate the benefits of combining clusteringvisualization and conceptual clustering to obtain bettercluster interpretations. In our research we have combinedconcept trees for conceptual clustering with shaded similaritymatrices for visualization. Experimentation shows thatthe two interpretation approaches can complement eachother to help us understand data better.