IEICE TRANSACTIONS on Information
Study of a Reasonable Initial Center Selection Method Applied to a K-Means Clustering
Summary :
Clustering methods are divided into hierarchical clustering, partitioning clustering, and more. K-Means is a method of partitioning clustering. We improve the performance of a K-Means, selecting the initial centers of a cluster through a calculation rather than using random selecting. This method maximizes the distance among the initial centers of clusters. Subsequently, the centers are distributed evenly and the results are more accurate than for initial cluster centers selected at random. This is time-consuming, but it can reduce the total clustering time by minimizing allocation and recalculation. Compared with the standard algorithm, F-Measure is more accurate by 5.1%.
- Publication
- IEICE TRANSACTIONS on Information Vol.E96-D No.8 pp.1727-1733
- Publication Date
- 2013/08/01
- Publicized
- Online ISSN
- 1745-1361
- DOI
- 10.1587/transinf.E96.D.1727
- Type of Manuscript
- PAPER
- Category
- Artificial Intelligence, Data Mining
Authors
WonHee LEE
Chonbuk National University
Samuel Sangkon LEE
Jeonju University
Dong-Un AN
Chonbuk National University
Keyword
Latest Issue
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WonHee LEE, Samuel Sangkon LEE, Dong-Un AN, "Study of a Reasonable Initial Center Selection Method Applied to a K-Means Clustering" in IEICE TRANSACTIONS on Information,
vol. E96-D, no. 8, pp. 1727-1733, August 2013, doi: 10.1587/transinf.E96.D.1727.
Abstract: Clustering methods are divided into hierarchical clustering, partitioning clustering, and more. K-Means is a method of partitioning clustering. We improve the performance of a K-Means, selecting the initial centers of a cluster through a calculation rather than using random selecting. This method maximizes the distance among the initial centers of clusters. Subsequently, the centers are distributed evenly and the results are more accurate than for initial cluster centers selected at random. This is time-consuming, but it can reduce the total clustering time by minimizing allocation and recalculation. Compared with the standard algorithm, F-Measure is more accurate by 5.1%.
URL: https://global.ieice.org/en_transactions/information/10.1587/transinf.E96.D.1727/_p
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@ARTICLE{e96-d_8_1727,
author={WonHee LEE, Samuel Sangkon LEE, Dong-Un AN, },
journal={IEICE TRANSACTIONS on Information},
title={Study of a Reasonable Initial Center Selection Method Applied to a K-Means Clustering},
year={2013},
volume={E96-D},
number={8},
pages={1727-1733},
abstract={Clustering methods are divided into hierarchical clustering, partitioning clustering, and more. K-Means is a method of partitioning clustering. We improve the performance of a K-Means, selecting the initial centers of a cluster through a calculation rather than using random selecting. This method maximizes the distance among the initial centers of clusters. Subsequently, the centers are distributed evenly and the results are more accurate than for initial cluster centers selected at random. This is time-consuming, but it can reduce the total clustering time by minimizing allocation and recalculation. Compared with the standard algorithm, F-Measure is more accurate by 5.1%.},
keywords={},
doi={10.1587/transinf.E96.D.1727},
ISSN={1745-1361},
month={August},}
Copy
TY - JOUR
TI - Study of a Reasonable Initial Center Selection Method Applied to a K-Means Clustering
T2 - IEICE TRANSACTIONS on Information
SP - 1727
EP - 1733
AU - WonHee LEE
AU - Samuel Sangkon LEE
AU - Dong-Un AN
PY - 2013
DO - 10.1587/transinf.E96.D.1727
JO - IEICE TRANSACTIONS on Information
SN - 1745-1361
VL - E96-D
IS - 8
JA - IEICE TRANSACTIONS on Information
Y1 - August 2013
AB - Clustering methods are divided into hierarchical clustering, partitioning clustering, and more. K-Means is a method of partitioning clustering. We improve the performance of a K-Means, selecting the initial centers of a cluster through a calculation rather than using random selecting. This method maximizes the distance among the initial centers of clusters. Subsequently, the centers are distributed evenly and the results are more accurate than for initial cluster centers selected at random. This is time-consuming, but it can reduce the total clustering time by minimizing allocation and recalculation. Compared with the standard algorithm, F-Measure is more accurate by 5.1%.
ER -
English
