Data Resampling for Path Based Clustering
Path Based Clustering assigns two objects to the same cluster if they are connected by a path with high similarity between adjacent objects on the path. In this paper, we propose a fast agglomerative algorithm to minimize the Path Based Clustering cost function. To enhance the reliability of the clustering results a stochastic resampling method is used to generate candidate solutions which are merged to yield empirical assignment probabilities of objects to clusters. The resampling algorithm measures the reliability of the clustering solution and, based on their stability, determines the number of clusters.