Self-organizing maps and clustering methods for matrix data
In this contribution we present extensions of the Self Organizing Map and clustering methods for the categorization and visualization of data which are described by matrices rather than feature vectors. Rows and Columns of these matrices correspond to objects which may or may not belong to the same set, and the entries in the matrix describe the relationships between them. The clustering task is formulated as an optimization problem: Model complexity is minimized under the constraint, that the error one makes when reconstructing objects from class information is fixed, usually to a small value. The data is then visualized with help of modified Self Organizing Maps methods, i.e. by constructing a neighborhood preserving non-linear projection into a low-dimensional map-space. Grouping of data objects is done using an improved optimization technique, which combines deterministic annealing with growing techniques. Performance of the new methods is evaluated by applying them to two kinds of matrix data: (i) pairwise data, where row and column objects are from the same set and where matrix elements denote dissimilarity values and (ii) co-occurrence data, where row and column objects are from different sets and where the matrix elements describe how often object pairs occur.
self-organizing maps matrix data deterministic annealing co-occurrence data pairwise clustering information bottleneck growing network rate distortion theory maximum entropy methods