ConDist: A Context-Driven Categorical Distance Measure
M. Ring, F. Otto, M. Becker, T. Niebler, D. Landes, and A. Hotho. Proceedings of the European Conference on Machine Learning and Knowledge Discovery in Databases
, volume 9284 of Lecture Notes in Computer Science, page 251-266. Springer International Publishing, (2015)
A distance measure between objects is a key requirement for many data mining tasks like clustering, classification or outlier detection. However, for objects characterized by categorical attributes, defining meaningful distance measures is a challenging task since the values within such attributes have no inherent order, especially without additional domain knowledge. In this paper, we propose an unsupervised distance measure for objects with categorical attributes based on the idea that categorical attribute values are similar if they appear with similar value distributions on correlated context attributes. Thus, the distance measure is automatically derived from the given data set. We compare our new distance measure to existing categorical distance measures and evaluate on different data sets from the UCI machine-learning repository. The experiments show that our distance measure is recommendable, since it achieves similar or better results in a more robust way than previous approaches.