CHARACTERIZATION AND DETECTION OF NOISE IN CLUSTERING

A concept of 'Noise Cluster' is introduced such that noisy data points may be assigned to the noise class. The approach is developed for objective functional type (K-means or fuzzy K-means) algorithms, and its ability to detect 'good' clusters amongst noisy data is demonstrated. The approach presented is applicable to a variety of fuzzy clustering algorithms as well as regression analysis.