The Effect of Noise and Outliers on Fuzzy Clustering of High Dimensional Data

Ludmila Himmelspach, Stefan Conrad

Abstract

Clustering high dimensional data is still a challenging problem for fuzzy clustering algorithms because distances between each pair of data items get similar with the increasing number of dimensions. The presence of noise and outliers in data is an additional problem for clustering algorithms because they might affect the computation of cluster centers. In this work, we analyze the effect of different kinds of noise and outliers on fuzzy clustering algorithms that can handle high dimensional data: FCM with attribute weighting, the multivariate fuzzy c-means (MFCM), and the possibilistic multivariate fuzzy c-means (PMFCM). Additionally, we propose a new version of PMFCM to enhance its ability handling noise and outliers in high dimensional data. The experimental results on different high dimensional data sets show that the possibilistic versions of MFCM produce accurate cluster centers independently of the kind of noise and outliers.

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Paper Citation


in Harvard Style

Himmelspach L. and Conrad S. (2016). The Effect of Noise and Outliers on Fuzzy Clustering of High Dimensional Data . In Proceedings of the 8th International Joint Conference on Computational Intelligence - Volume 2: FCTA, (IJCCI 2016) ISBN 978-989-758-201-1, pages 101-108. DOI: 10.5220/0006070601010108


in Bibtex Style

@conference{fcta16,
author={Ludmila Himmelspach and Stefan Conrad},
title={The Effect of Noise and Outliers on Fuzzy Clustering of High Dimensional Data},
booktitle={Proceedings of the 8th International Joint Conference on Computational Intelligence - Volume 2: FCTA, (IJCCI 2016)},
year={2016},
pages={101-108},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0006070601010108},
isbn={978-989-758-201-1},
}


in EndNote Style

TY - CONF
JO - Proceedings of the 8th International Joint Conference on Computational Intelligence - Volume 2: FCTA, (IJCCI 2016)
TI - The Effect of Noise and Outliers on Fuzzy Clustering of High Dimensional Data
SN - 978-989-758-201-1
AU - Himmelspach L.
AU - Conrad S.
PY - 2016
SP - 101
EP - 108
DO - 10.5220/0006070601010108