Abstract
In cluster analysis, the mode boundaries are a very important part of the hierarchy of structures that link raw data with their interpretation. The existing mode boundary detection approaches for clustering are conditioned by the adjustment of some parameters, which become critical for large dimensionality data sets.
Mode boundary detection can be greatly facilitate by mapping, as a first step of process understanding, a reduction of data dimensionality. Under this assumption, an approach is discussed, based on both neural network and mathematical morphology. It requires neither a starting classification, nor an a priori number of clusters or their distribution.
Data projection mapping is done using a multilayer neural network with a fast training rule based on a conjugate gradient. Mode boundaries of the underlying probability density function, estimated from the patterns in the projection space, are then easily obtained by making concepts of morphological watershed transformations suitable for their detection. The observations in the raw data space corresponding to those falling in the so-detected mode boundaries are taken as prototypes for classification.
The clustering scheme, illustrated using an artificial simulation, has been applied to determine the clusters inside a set of biometrical six-dimensional data of the Guadeloupe honeybee's races.