Paper Info
Title
Voronoi Cell-Based Clustering Using a Kernel Support
Abstract
Support-based clustering using kernels suffers from serious computational limitations inherent in many kernel methods when applied to very large-scale problems despite its ability to identify clusters with complex shapes. In this paper, we propose a novel clustering algorithm called Voronoi cell-based clustering to expedite support-based clustering using kernels. In contrast to previous studies, including the basin cell-based method, the proposed method achieves computational efficiency in both the training phase to construct a support estimate using sampled data to reduce the evaluation of kernels and the labeling phase to assign a cluster label on each data point nearest its representative point. The performance superiority of the proposed method over the other basin cell-based methods in terms of computational time and storage efficiency is verified by various experiments using benchmark sets and in real applications to image segmentation.
Year
DOI
Venue
2015
10.1109/TKDE.2014.2359662
IEEE Trans. Knowl. Data Eng.
Keywords
Field
DocType
voronoi cell-based clustering algorithm,kernel support,storage efficiency,computational time,pattern clustering,kernel methods,cluster label,support-based clustering,image segmentation,computational geometry,kernel evaluation,data point,clustering,basin cell-based method,support level function,support vector machines,clustering algorithms,kernel,estimation,algorithm design and analysis,labeling
Data mining,Fuzzy clustering,CURE data clustering algorithm,Computer science,Artificial intelligence,Cluster analysis,k-medians clustering,Canopy clustering algorithm,Clustering high-dimensional data,Data stream clustering,Correlation clustering,Pattern recognition,Machine learning
Journal
Volume
Issue
ISSN
27
4
1041-4347
Citations 
PageRank 
References 
4
0.39
21
Authors
3
Name
Order
Citations
PageRank
Kyoungok Kim1352.36
Youngdoo Son2103.17
Jaewook Lee3728.87