On-line robust fuzzy clustering based on similarity measure

Abstract

The problem offuzzy adaptive on-line clustering of data distorted by outliers sequentially supplied to the processing when the original sample volume and the number of distorted observations are unknown is considered. The probabilistic and possibilistic clustering algorithms for such data, that are based on the similarity measure of a special kind that weakens or overwhelming outliers are proposed.