TY - GEN A1 - Weber, Marcus T1 - Improved Perron Cluster Analysis N2 - The problem of clustering data can often be transformed into the problem of finding a hidden block diagonal structure in a stochastic matrix. Deuflhard et al. have proposed an algorithm that state s the number $k$ of clusters and uses the sign structure of $k$ eigenvectors of the stochastic matrix to solve the cluster problem. Recently Weber and Galliat discovered that this system of eigenvectors can easily be transformed into a system of $k$ membership functions or soft characteristic functions describing the clusters. In this article we explain the corresponding cluster algorithm and point out the underlying theory. By means of numerical examples we explain how the grade of membership can be interpreted. T3 - ZIB-Report - 03-04 KW - cluster analysis KW - stochastic matrices KW - almost invariant sets Y1 - 2003 UR - https://opus4.kobv.de/opus4-zib/frontdoor/index/index/docId/726 UR - https://nbn-resolving.org/urn:nbn:de:0297-zib-7260 ER -