Additional speed-up technique to fuzzy clustering using a multiresolution approach

The fuzzy clustering algorithm (FCA) is a powerful tool for unsupervised investigation of complex data in functional ME. The original, computationally very expensive algorithm has been adapted in various ways to increase its performance while keeping it stable and sensitive. A simple and highly efficient way to speed up the FCA is preselection (screening) of potentially interesting time-courses, in a way that those time-courses, where only noise is expected are discarded. Although quite successful, preselecting data by some criterion is a step back to model driven analysis and should therefore be used with deliberation. Furthermore, some screening methods run the risk of missing non-periodic signals. We propose an additional adaptation using a multi-resolution approach that first scales down the data volumes. Starting with the lowest resolution, the FCA is applied to that level and then, the computed centroids are used as initial values to the FCA for the next higher level of resolution and so on until the original resolution is reached. The processing of all lower resolution levels serves as a good and fast initialization of the FCA, resulting in a stable convergence and an improved performance without loss of information.