Novel support vector clustering with label assignment in enriched neighborhood

Authors:
Ling Ping;Gao Dajin;Huo Fujiang;Rong Xiangsheng;You Xiangyang
Affiliations:
School of Computer Science, Xuzhou Normal University, Xuzhou, China;Training Department, Xuzhou Air Force College of P. L. A, Xuzhou, China;Department of Logistic Command, Xuzhou Air Force College of P. L. A, Xuzhou, China;Training Department, Xuzhou Air Force College of P. L. A, Xuzhou, China;Training Department, Xuzhou Air Force College of P. L. A, Xuzhou, China
Venue:
FSKD'09 Proceedings of the 6th international conference on Fuzzy systems and knowledge discovery - Volume 1
Year:
2009

Citing 9
Cited 0

C4.5: programs for machine learning

C4.5: programs for machine learning
The nature of statistical learning theory

The nature of statistical learning theory
Discriminant Adaptive Nearest Neighbor Classification

IEEE Transactions on Pattern Analysis and Machine Intelligence
Classification by pairwise coupling

NIPS '97 Proceedings of the 1997 conference on Advances in neural information processing systems 10
Normalized Cuts and Image Segmentation

CVPR '97 Proceedings of the 1997 Conference on Computer Vision and Pattern Recognition (CVPR '97)
On clusterings-good, bad and spectral

FOCS '00 Proceedings of the 41st Annual Symposium on Foundations of Computer Science
Support vector clustering

The Journal of Machine Learning Research
Statistical Classification of Raw Textile Defects

ICPR '04 Proceedings of the Pattern Recognition, 17th International Conference on (ICPR'04) Volume 4 - Volume 04
Mercer kernel-based clustering in feature space

IEEE Transactions on Neural Networks

Quantified Score

Hi-index	0.00

Visualization

Abstract

Support vector clustering (SVC) is an appealing approach that can detect cluster boundaries. In spite of its popularization in applications, it sees the critical bottleneck in cluster labeling. This paper presents a Novel Support Vector Clustering algorithm (NSVC) to go a further step in clustering labeling. NSVC consists of three phases: extract Data Representatives (DRs); cluster DRs; label non-DR data. The objective of traditional SVC is used by NSVC for finding DRs, but the Kernel scale of the objective is modified. DRs are grouped by Spectrum Analysis (SA) method, which simultaneously develops an informative metric. Non-DR data are labeled by a weighted kNN procedure that works in query's neighborhood, which is formulated with the new metric and then enriched by the convex hull skill. Experiments on real datasets demonstrate the improvement of NSVC over its peers and the competitive performance with the state of the arts.