Robust Clustering Algorithms and Potential Applications - Algorithms for robust data clustering, image segmentation and data classification

by Yang, Xu-Lei
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Yang, Xu-Lei Robust Clustering Algorithms and Potential  Applications - Algorithms for robust data clustering, image  segmentation and data classification
Yang, Xu-Lei - Robust Clustering Algorithms and Potential  Applications - Algorithms for robust data clustering, image  segmentation and data classification

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Description

Several novel and robust learning algorithms, with
the aim to overcome the drawbacks of traditional
clustering algorithms, are developed for data
clustering and its applications. The effectiveness
and superiority of the proposed methods are
supported by experimental results.
1) Te proposed RDA exhibits several robust
clustering characteristics: robust to the
initialization; robust to cluster volumes; and
robust to noise and outliers.
2) The proposed IFCSS algorithm achieves two robust
clustering characteristics: the robustness
against noisy points is obtained by the maximization
of mutual information; and the optimal cluster
number is auto-determined by the VC-bound induced
cluster validity.
3) The KDA is developed to discover some complicated
(e.g., linearly nonseparable) data structures which
can not be revealed by traditional clustering
methods in the standard Euclidean space.
4) Finally, robust clustering methods have been
developed for image segmentation and pattern
classification. The proposed ASDA can perform
unsupervised clustering for robust image
segmentation. The KPCM is developed to generate
weights used for SVM training.

Contributors

Author:
Yang, Xu-Lei

Further information

Biography Artist:
XuLei YANG obtained the PhD degree from EEE School, NTU in
2005. His current research interests include pattern
recognition, image processing, and machine vision. He has
published more than 20 papers in scientific book chapters,
journals and conference proceedings.
Language:
English
Number of Pages:
192
Media Type:
Softcover
Publisher:
VDM Verlag Dr. Müller

Master Data

Product Type:
Paperback book
Release date:
19 July 2009
Package Dimensions:
0.22 x 0.15 x 0.011 m; 0.336 kg
GTIN:
09783639180695
DUIN:
RL6ADUM3DR8
£58.99
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