by Li, F and Klette, R
Abstract:
This paper proposes a special adaptive mean shift clustering algorithm, especially for the case of highly overlapping clusters. Its application is demonstrated for simulated data, aiming at finding the ‘old clusters’. The obtained clustering result is actually close to an estimated upper bound, derived for those simulated data elsewhere. © 2009 Springer Berlin Heidelberg.
Reference:
A variant of adaptive mean shift-based clustering (Li, F and Klette, R), In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), volume 5506 LNCS, 2009.
Bibtex Entry:
@inproceedings{li2009aclustering,
author = "Li, F and Klette, R",
booktitle = "Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)",
pages = "1002--1009",
title = "A variant of adaptive mean shift-based clustering",
volume = "5506 LNCS",
year = "2009",
abstract = "This paper proposes a special adaptive mean shift clustering algorithm, especially for the case of highly overlapping clusters. Its application is demonstrated for simulated data, aiming at finding the 'old clusters'. The obtained clustering result is actually close to an estimated upper bound, derived for those simulated data elsewhere. © 2009 Springer Berlin Heidelberg.",
doi = "10.1007/978-3-642-02490-0_122",
isbn = "3642024890",
isbn = "9783642024894",
issn = "0302-9743",
eissn = "1611-3349",
issue = "PART 1",
language = "eng",
}