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Handling Noise and Outliers in Fuzzy Clustering
Type of publication: Incollection
Citation:
Booktitle: Fifty Years of Fuzzy Logic and its Applications
Series: Studies in Fuzziness and Soft Computing
Volume: 326
Year: 2015
Pages: 315-335
Publisher: Springer International Publishing
ISBN: 978-3-319-19682-4
URL: http://link.springer.com/chapt...
DOI: 10.1007/978-3-319-19683-1_17
Abstract: Since it is an unsupervised data analysis approach, clustering relies solely on the location of the data points in the data space or, alternatively, on their relative distances or similarities. As a consequence, clustering can suffer from the presence of noisy data points and outliers, which can obscure the structure of the clusters in the data and thus may drive clustering algorithms to yield suboptimal or even misleading results. Fuzzy clustering is no exception in this respect, although it features an aspect of robustness, due to which outliers and generally data points that are atypical for the clusters in the data have a lesser influence on the cluster parameters. Starting from this aspect, we provide in this paper an overview of different approaches with which fuzzy clustering can be made less sensitive to noise and outliers and categorize them according to the component of standard fuzzy clustering they modify.
Keywords:
Authors Borgelt, Christian
Braune, Christian
Lesot, Marie-Jeanne
Kruse, Rudolf
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