@misc{WeberRungsarityotinSchliep, author = {Weber, Marcus and Rungsarityotin, Wasinee and Schliep, Alexander}, title = {Perron Cluster Analysis and Its Connection to Graph Partitioning for Noisy Data}, url = {http://nbn-resolving.de/urn:nbn:de:0297-zib-8140}, number = {04-39}, abstract = {The problem of clustering data can be formulated as a graph partitioning problem. Spectral methods for obtaining optimal solutions have reveceived a lot of attention recently. We describe Perron Cluster Cluster Analysis (PCCA) and, for the first time, establish a connection to spectral graph partitioning. We show that in our approach a clustering can be efficiently computed using a simple linear map of the eigenvector data. To deal with the prevalent problem of noisy and possibly overlapping data we introduce the min Chi indicator which helps in selecting the number of clusters and confirming the existence of a partition of the data. This gives a non-probabilistic alternative to statistical mixture-models. We close with showing favorable results on the analysis of gene expressi on data for two different cancer types.}, language = {en} } @incollection{WeberRungsarityotinSchliep2006, author = {Weber, Marcus and Rungsarityotin, Wasinee and Schliep, Alexander}, title = {An Indicator for the Number of Clusters}, series = {From Data and Information Analysis to Knowledge Engineering}, booktitle = {From Data and Information Analysis to Knowledge Engineering}, editor = {Spiliopoulou, Myra and Kruse, Rudolf and Borgelt, Christian and N{\"u}rnberger, Andreas and Gaul, Wolfgang}, publisher = {Springer Berlin Heidelberg}, pages = {103 -- 110}, year = {2006}, language = {en} }