A KNN-Scoring Based Core-Growing Approach to Cluster Analysis

Authors:
T. W. Hsieh;J. S. Taur;S. Y. Kung
Affiliations:
Department of Electrical Engineering, National Chung Hsing University, Taichung, Republic of China;Department of Electrical Engineering, National Chung Hsing University, Taichung, Republic of China;Department of Electrical Engineering, Princeton University, Princeton, USA
Venue:
Journal of Signal Processing Systems
Year:
2010

Citing 5
Cited 0

Algorithms for clustering data

Algorithms for clustering data
A Novel Kernel Method for Clustering

IEEE Transactions on Pattern Analysis and Machine Intelligence
A Comparison of Fuzzy Clustering Approaches for Quantification of Microarray Gene Expression

Journal of Signal Processing Systems
Soft learning vector quantization and clustering algorithms based on non-Euclidean norms: single-norm algorithms

IEEE Transactions on Neural Networks
Survey of clustering algorithms

IEEE Transactions on Neural Networks

Quantified Score

Hi-index	0.00

Visualization

Abstract

This paper proposes a novel core-growing (CG) clustering method based on scoring k-nearest neighbors (CG-KNN). First, an initial core for each cluster is obtained, and then a tree-like structure is constructed by sequentially absorbing data points into the existing cores according to the KNN linkage score. The CG-KNN can deal with arbitrary cluster shapes via the KNN linkage strategy. On the other hand, it allows the membership of a previously assigned training pattern to be changed to a more suitable cluster. This is supposed to enhance the robustness. Experimental results on four UCI real data benchmarks and Leukemia data sets indicate that the proposed CG-KNN algorithm outperforms several popular clustering algorithms, such as Fuzzy C-means (FCM) (Xu and Wunsch IEEE Transactions on Neural Networks 16:645---678, 2005), Hierarchical Clustering (HC) (Xu and Wunsch IEEE Transactions on Neural Networks 16:645---678, 2005), Self-Organizing Maps (SOM) (Golub et al. Science 286:531---537, 1999; Tamayo et al. Proceedings of the National Academy of Science USA 96:2907, 1999), and Non-Euclidean Norm FCM (NEFCM) (Karayiannis and Randolph-Gips IEEE Transactions On Neural Networks 16, 2005).