Open Access System for Information Sharing

Login Library

 

Article
Cited 29 time in webofscience Cited 35 time in scopus
Metadata Downloads

Dynamic Dissimilarity Measure for Support-Based Clustering SCIE SCOPUS

Title
Dynamic Dissimilarity Measure for Support-Based Clustering
Authors
Lee, DLee, J
Date Issued
2010-06
Publisher
IEEE COMPUTER SOC
Abstract
Clustering methods utilizing support estimates of a data distribution have recently attracted much attention because of their ability to generate cluster boundaries of arbitrary shape and to deal with outliers efficiently. In this paper, we propose a novel dissimilarity measure based on a dynamical system associated with support estimating functions. Theoretical foundations of the proposed measure are developed and applied to construct a clustering method that can effectively partition the whole data space. Simulation results demonstrate that clustering based on the proposed dissimilarity measure is robust to the choice of kernel parameters and able to control the number of clusters efficiently.
Keywords
Clustering; kernel methods; dynamical systems; equilibrium vector; support; VECTOR; CLASSIFICATION; OPTIMIZATION
URI
https://oasis.postech.ac.kr/handle/2014.oak/25887
DOI
10.1109/TKDE.2009.140
ISSN
1041-4347
Article Type
Article
Citation
IEEE TRANSACTIONS ON KNOWLEDGE AND DATA ENGINEERING, vol. 22, no. 6, page. 900 - 905, 2010-06
Files in This Item:
There are no files associated with this item.

qr_code

  • mendeley

Items in DSpace are protected by copyright, with all rights reserved, unless otherwise indicated.

Related Researcher

Researcher

이재욱LEE, JAEWOOK
Dept of Industrial & Management Enginrg
Read more

Views & Downloads

Browse