An EM-Approach for Clustering Multi-Instance Objects

In many data mining applications the data objects are modeled as sets of feature vectors or multi-instance objects. In this paper, we present an expectation maximization approach for clustering multiinstance objects. We therefore present a statistical process that models multi-instance objects. Furthermore, we present M-steps and E-steps for EM clustering and a method for finding a good initial model. In our experimental evaluation, we demonstrate that the new EM algorithm is capable to increase the cluster quality for three real world data sets compared to a k-medoid clustering.