Distance Based Clustering for Categorical Data

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.