An update algorithm for restricted random walk clustering for dynamic data sets

Abstract

In this article, we present a randomized dynamic cluster algorithm for large data sets. It is based on the restricted random walk cluster algorithm by Scholl and Scholl-Paschinger that has given good results in past studies. We discuss different approaches for the clustering of dynamic data sets. In contrast to most of these methods, dynamic restricted random walk clustering is also efficient for a small percentage of changes in the data set and has the additional advantage that the updates asymptotically produce the same clusters as a reclustering with the static variant; there is thus no need for any reclustering ever. In addition, the method has a relatively low computational complexity which enables it to cluster large data sets.