Learning distances from categorical attributes is a very useful data mining task that allows to perform distance-based techniques, such as clustering and classification by similarity. In this article we propose a new context-based similarity measure that learns distances between the values of a categorical attribute (DILCA - DIstance Learning of Categorical Attributes). We couple our similarity measure with a famous hierarchical distance-based clustering algorithm (Ward's hierarchical clustering) and compare the results with the results obtained from methods of the state of the art for this research field.

Distance Based Clustering for Categorical Data

IENCO, Dino;MEO, Rosa
2009

Abstract

Learning distances from categorical attributes is a very useful data mining task that allows to perform distance-based techniques, such as clustering and classification by similarity. In this article we propose a new context-based similarity measure that learns distances between the values of a categorical attribute (DILCA - DIstance Learning of Categorical Attributes). We couple our similarity measure with a famous hierarchical distance-based clustering algorithm (Ward's hierarchical clustering) and compare the results with the results obtained from methods of the state of the art for this research field.
Proceedings of the 17th Italian Symposium on Advanced Database Systems
Camogli (Genova)
21-24 June 2009
Proceedings of the 17th Italian Symposium on Advanced Database Systems
Dipartimento di Informatica e Scienze dell'Informazione dell'Università di Genova
281
288
9788861221543
http://sebd09.disi.unige.it/index.html
learning distance; categorical variable; context; hierarchical clustering
Ienco, Dino; Meo, Rosa
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/2318/60836
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