@inproceedings{f23ea1c89c3a459fbc549d80bb26bc0b,
title = "Identification of noise outliers in clustering by a fuzzy neural network",
abstract = "Since most real data sets, encountered in cluster analysis are contaminated with background noise or outliers, it is essential to detect and isolate these noise samples from the data set. A few noisy points can affect the clustering procedure by severely biasing the algorithm. An ideal solution to this problem is to identify all the outliers and form a separate noise cluster. In order to do so, we need a technique by which the noise points are automatically identified and removed from the pattern data. This paper presents a modified Adaptive Fuzzy Leader Clustering (AFLC) algorithm that has been used to detect and eliminate the outliers from the data structure and create a separate cluster of the outliers. The AFLC algorithm has an Adaptive Resonance Theory (ART) like architecture with fuzzy learning rules embedded into it.",
author = "Surya Pemmaraju and Sunanda Mitra",
note = "Copyright: Copyright 2004 Elsevier B.V., All rights reserved.; null ; Conference date: 28-03-1993 Through 01-04-1993",
year = "1993",
language = "English",
isbn = "0780306155",
series = "1993 IEEE International Conference on Fuzzy Systems",
publisher = "Publ by IEEE",
pages = "1269--1274",
booktitle = "1993 IEEE International Conference on Fuzzy Systems",
}