Clustering of noisy image data using an adaptive neuro-fuzzy system
Suryalakshmi Pemmaraju, Sunanda Mitra · Proceedings of SPIE, the International Society for Optical Engineering/Proceedings of SPIE · 1992
Identification of outliers or noise in a real data set is often quite difficult. A recently developed adaptive fuzzy leader clustering (AFLC) algorithm has been modified to separate the outliers from real data sets while finding the clusters within the data sets. The capability of this modified AFLC algorithm to identify the outliers in a number of real data sets indicates the potential strength of this algorithm in correct classification of noise real data.