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2017 | OriginalPaper | Buchkapitel

Robust Clustering Algorithms Employing Fuzzy-Possibilistic Product Partition

verfasst von : László Szilágyi

Erschienen in: Fuzzy Sets, Rough Sets, Multisets and Clustering

Verlag: Springer International Publishing

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Abstract

One of the main challenges in the field of clustering is creating algorithms that are both accurate and robust. The fuzzy-possibilistic product partition c-means clustering algorithm was introduced with the main goal of producing accurate partitions in the presence of outlier data. This chapter presents several clustering algorithms based on the fuzzy-possibilistic product partition, specialized for the detection of clusters having various shapes including spherical and ellipsoidal shells. The advantages of applying the fuzzy-possibilistic product partition are presented in comparison with previous c-means clustering models. Besides being more robust and accurate than previous probabilistic-possibilistic mixture partitions, the product partition is easier to handle due to its reduced number of parameters.

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Titel: Robust Clustering Algorithms Employing Fuzzy-Possibilistic Product Partition
verfasst von: László Szilágyi
Verlag: Springer International Publishing
Buch: Fuzzy Sets, Rough Sets, Multisets and Clustering
Print ISBN: 978-3-319-47556-1

Electronic ISBN: 978-3-319-47557-8

Copyright-Jahr: 2017
DOI: https://doi.org/10.1007/978-3-319-47557-8_7

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