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Class dependent cluster refinement

Sternby, Jakob LU (2006) 18th International Conference on Pattern Recognition, ICPR 2006 2. p.833-836
Abstract
Unsupervised classification is a very common problem in pattern recognition even when the classes are known. In many areas intra-class variations may be greater than the inter-class variations causing a need for a subdivision of the training set of a class into smaller subunits often referred to as clusters. The subdivision or clustering is often performed independently of the relative properties of the other present classes in the recognition task. This paper presents a novel class-dependent approach to the clustering problem. Experiments with online handwriting data show that the novel clustering approach CDCR produces a clustering better suited for the task of pattern recognition. Although only validated for two recognition methods in... (More)
Unsupervised classification is a very common problem in pattern recognition even when the classes are known. In many areas intra-class variations may be greater than the inter-class variations causing a need for a subdivision of the training set of a class into smaller subunits often referred to as clusters. The subdivision or clustering is often performed independently of the relative properties of the other present classes in the recognition task. This paper presents a novel class-dependent approach to the clustering problem. Experiments with online handwriting data show that the novel clustering approach CDCR produces a clustering better suited for the task of pattern recognition. Although only validated for two recognition methods in this paper, the same approach could be applied to other methods as well as to other pattern recognition problems. (Less)
Please use this url to cite or link to this publication:
author
organization
publishing date
type
Chapter in Book/Report/Conference proceeding
publication status
published
subject
keywords
Unsupervised classification, Clustering problems
host publication
Proceedings - International Conference on Pattern Recognition
volume
2
pages
833 - 836
publisher
IEEE - Institute of Electrical and Electronics Engineers Inc.
conference name
18th International Conference on Pattern Recognition, ICPR 2006
conference location
Hong Kong, China
conference dates
2006-08-20 - 2006-08-24
external identifiers
  • wos:000240678300201
  • scopus:34047223472
ISSN
1051-4651
DOI
10.1109/ICPR.2006.364
language
English
LU publication?
yes
id
4657fd65-8e37-4fe1-bbc6-b32d54b96c40 (old id 616651)
date added to LUP
2016-04-01 15:21:57
date last changed
2021-02-17 07:39:58
@inproceedings{4657fd65-8e37-4fe1-bbc6-b32d54b96c40,
  abstract     = {Unsupervised classification is a very common problem in pattern recognition even when the classes are known. In many areas intra-class variations may be greater than the inter-class variations causing a need for a subdivision of the training set of a class into smaller subunits often referred to as clusters. The subdivision or clustering is often performed independently of the relative properties of the other present classes in the recognition task. This paper presents a novel class-dependent approach to the clustering problem. Experiments with online handwriting data show that the novel clustering approach CDCR produces a clustering better suited for the task of pattern recognition. Although only validated for two recognition methods in this paper, the same approach could be applied to other methods as well as to other pattern recognition problems.},
  author       = {Sternby, Jakob},
  booktitle    = {Proceedings - International Conference on Pattern Recognition},
  issn         = {1051-4651},
  language     = {eng},
  pages        = {833--836},
  publisher    = {IEEE - Institute of Electrical and Electronics Engineers Inc.},
  title        = {Class dependent cluster refinement},
  url          = {http://dx.doi.org/10.1109/ICPR.2006.364},
  doi          = {10.1109/ICPR.2006.364},
  volume       = {2},
  year         = {2006},
}