Least-biased fuzzy clustering method for inhomogeneous data

We have extended the least biased fuzzy clustering algorithm to inhomogeneous data sets. The resolution parameter is generalized from a scalar to a vector with the dimension of the feature space. We fix the orientation of the resolution vector to measure the relative inhomogeneities of each cluster of data points in the different dimensions; and we study the effect of the magnitude of the resolution parameter on the phase transitions yielding the clusters. Based on the detection of the onset of a phase transition, a new technique for truncating the iteration scheme of solution reduces the computational complexity to the order of the number of data points. The actual computational load of the algorithm is discussed and examples are given to illustrate the performance of the algorithm in clustering inhomogeneous data sets.© (1995) COPYRIGHT SPIE--The International Society for Optical Engineering. Downloading of the abstract is permitted for personal use only.