Li, J, Song, S, Zhang, Y et al. (1 more author) (2017) A Robust Fuzzy c-Means Clustering Algorithm for Incomplete Data. In: Yue, D, Peng, C, Du, D, Zhang, T, Zheng, M and Han, Q, (eds.) Intelligent Computing, Networked Control, and Their Engineering Applications. LSMS 2017: International Conference on Life System Modeling and Simulation, and ICSEE 2017: International Conference on Intelligent Computing for Sustainable Energy and Environment, 22-24 Sep 2017, Nanjing, China. Springer , pp. 3-12. ISBN 978-981-10-6372-5
Abstract
Date sets with missing feature values are prevalent in clustering analysis. Most existing clustering methods for incomplete data rely on imputations of missing feature values. However, accurate imputations are usually hard to obtain especially for small-size or highly corrupted data sets. To address this issue, this paper proposes a robust fuzzy c-means (RFCM) clustering algorithm, which does not require imputations. The proposed RFCM represents the missing feature values by intervals, which can be easily constructed using the K-nearest neighbors method, and adopts a min-max optimization model to reduce the impact of noises on clustering performance. We give an equivalent tractable reformulation of the min-max optimization problem and propose an efficient solution method based on smoothing and gradient projection techniques. Experiments on UCI data sets validate the effectiveness of the proposed RFCM algorithm by comparison with existing clustering methods for incomplete data.
Metadata
Item Type: | Proceedings Paper |
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Authors/Creators: |
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Editors: |
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Keywords: | Robust FCM; Interval data; Robust clustering algorithm |
Dates: |
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Institution: | The University of Leeds |
Academic Units: | The University of Leeds > Faculty of Engineering & Physical Sciences (Leeds) > School of Electronic & Electrical Engineering (Leeds) > Institute of Communication & Power Networks (Leeds) |
Depositing User: | Symplectic Publications |
Date Deposited: | 23 Nov 2018 11:09 |
Last Modified: | 06 Mar 2019 14:22 |
Status: | Published |
Publisher: | Springer |
Identification Number: | 10.1007/978-981-10-6373-2_1 |
Open Archives Initiative ID (OAI ID): | oai:eprints.whiterose.ac.uk:139095 |