Adaptive fuzzy clustering for data with missing values based on the nearest prototype - centroid strategy
The problem of clustering vector data sets with missing values in some components is considered. The adaptive approach to clustering of data in situation then classes overlap is proposed. The basis of the approach is the using of the modified Kohonen maps with the neighborhood function of special kind.