Clustering Using a Random Walk Based Distance Measure

This work proposes a simple way to improve a clustering algorithm. The idea is to exploit a new distance metric called the “Euclidian Commute Time” (ECT) distance, based on a random walk model on a graph derived from the data. Using this distance measure instead of the usual Euclidean distance in a k-means algorithm allows to retrieve wellseparated clusters of arbitrary shape, without working hypothesis about their data distribution. Experimental results show that the use of this new distance measure significantly improves the quality of the clustering on the tested data sets.