Adaptive Clustering Technique Using Genetic Algorithms
Summary :
This paper proposes a genetically inspired adaptive clustering algorithm for numerical and categorical data sets. To this end, unique encoding method and fitness functions are developed. The algorithm automatically discovers the actual number of clusters and efficiently performs clustering without unduly compromising cluster-purity. Moreover, it outperforms existing clustering algorithms.
- Publication
- IEICE TRANSACTIONS on Information Vol.E88-D No.12 pp.2880-2882
- Publication Date
- 2005/12/01
- Publicized
- Online ISSN
- DOI
- 10.1093/ietisy/e88-d.12.2880
- Type of Manuscript
- LETTER
- Category
- Data Mining
Authors
Keyword
Latest Issue
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Nam Hyun PARK, Chang Wook AHN, Rudrapatna S. RAMAKRISHNA, "Adaptive Clustering Technique Using Genetic Algorithms" in IEICE TRANSACTIONS on Information,
vol. E88-D, no. 12, pp. 2880-2882, December 2005, doi: 10.1093/ietisy/e88-d.12.2880.
Abstract: This paper proposes a genetically inspired adaptive clustering algorithm for numerical and categorical data sets. To this end, unique encoding method and fitness functions are developed. The algorithm automatically discovers the actual number of clusters and efficiently performs clustering without unduly compromising cluster-purity. Moreover, it outperforms existing clustering algorithms.
URL: https://global.ieice.org/en_transactions/information/10.1093/ietisy/e88-d.12.2880/_p
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@ARTICLE{e88-d_12_2880,
author={Nam Hyun PARK, Chang Wook AHN, Rudrapatna S. RAMAKRISHNA, },
journal={IEICE TRANSACTIONS on Information},
title={Adaptive Clustering Technique Using Genetic Algorithms},
year={2005},
volume={E88-D},
number={12},
pages={2880-2882},
abstract={This paper proposes a genetically inspired adaptive clustering algorithm for numerical and categorical data sets. To this end, unique encoding method and fitness functions are developed. The algorithm automatically discovers the actual number of clusters and efficiently performs clustering without unduly compromising cluster-purity. Moreover, it outperforms existing clustering algorithms.},
keywords={},
doi={10.1093/ietisy/e88-d.12.2880},
ISSN={},
month={December},}
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TY - JOUR
TI - Adaptive Clustering Technique Using Genetic Algorithms
T2 - IEICE TRANSACTIONS on Information
SP - 2880
EP - 2882
AU - Nam Hyun PARK
AU - Chang Wook AHN
AU - Rudrapatna S. RAMAKRISHNA
PY - 2005
DO - 10.1093/ietisy/e88-d.12.2880
JO - IEICE TRANSACTIONS on Information
SN -
VL - E88-D
IS - 12
JA - IEICE TRANSACTIONS on Information
Y1 - December 2005
AB - This paper proposes a genetically inspired adaptive clustering algorithm for numerical and categorical data sets. To this end, unique encoding method and fitness functions are developed. The algorithm automatically discovers the actual number of clusters and efficiently performs clustering without unduly compromising cluster-purity. Moreover, it outperforms existing clustering algorithms.
ER -
English
