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

Xu-Lei Yang

ISBN 10: 3639180690 / ISBN 13: 9783639180695

Published by VDM Verlag Dr. Müller, 2009

About this Item

Robust Clustering Algorithms and Potential Applications | Algorithms for robust data clustering, image segmentation and data classification | Xu-Lei Yang | Taschenbuch | Englisch | VDM Verlag Dr. Müller | EAN 9783639180695 | Verantwortliche Person für die EU: preigu GmbH & Co. KG, Lengericher Landstr. 19, 49078 Osnabrück, mail[at]preigu[dot]de | Anbieter: preigu.

Seller Inventory # 101521090

Bibliographic Details

• Title: Robust Clustering Algorithms and Potential Applications | Algorithms for robust data clustering, image segmentation and data classification
• Publisher: VDM Verlag Dr. Müller
• Publication Date: 2009
• Language: English
• ISBN 10: 3639180690
• ISBN 13: 9783639180695
• Binding: Taschenbuch
• Condition: Neu
• Dimensions: 150W x 11H x 219D millimeters 150 millimeters width by 11 millimeters height by 219 millimeters depth
• Weight: 335 grams
• Seller catalogs:
  • Bücher

About this title

Synopsis

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.

About the Author

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.