A Survey of Distance Metrics for Nominal Attributes

Many distance-related algorithms, such as knearest neighbor learning algorithms, locally weighted learning algorithms etc, depend upon a good distance metric to be successful. In this kind of algorithms, a key problem is how to measure the distance between each pair of instances. In this paper, we provide a survey on distance metrics for nominal attributes, including some basic distance metrics and their improvements based on attribute weighting and attribute selection. The experimental results on the whole 36 UCI datasets published on the main web site of Weka platform validate their effectiveness.