Detecting critical regions in multidimensional data sets

We propose a new approach, based on the Conley index theory, for the detection and classification of critical regions in multidimensional data sets. The use of homology groups makes this method consistent and successful in all dimensions and allows to generalize visual classification techniques based solely on the notion of connectedness which may fail in higher dimensions.

• Type: Journal
• Year: 2011
• Where: CMA
• Authors: Madjid Allili, David Corriveau, Sara Derivière, Marc Ethier, Tomasz Kaczynski
• Keywords: CMA 2011, Conley Index Theory, Distributed And Parallel Computing, Multidimensional Data Sets, Visual Classification Techniques