Identification of noise outliers in clustering by a fuzzy neural network

Surya Pemmaraju, Sunanda Mitra

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

7 Scopus citations

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.

Original languageEnglish
Title of host publication1993 IEEE International Conference on Fuzzy Systems
PublisherPubl by IEEE
Pages1269-1274
Number of pages6
ISBN (Print)0780306155
StatePublished - 1993
EventSecond IEEE International Conference on Fuzzy Systems - San Francisco, CA, USA
Duration: Mar 28 1993Apr 1 1993

Publication series

Name1993 IEEE International Conference on Fuzzy Systems

Conference

ConferenceSecond IEEE International Conference on Fuzzy Systems
CitySan Francisco, CA, USA
Period03/28/9304/1/93

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