IEICE TRANSACTIONS on Information
Factor Controlled Hierarchical SOM Visualization for Large Set of Data
Summary :
Self-organizing map is a widely used tool in high-dimensional data visualization. However, despite its benefits of plotting very high-dimensional data on a low-dimensional grid, browsing and understanding the meaning of a trained map turn to be a difficult task -- specially when number of nodes or the size of data increases. Though there are some well-known techniques to visualize SOMs, they mainly deals with cluster boundaries and they fail to consider raw information available in original data in browsing SOMs. In this paper, we propose our Factor controlled Hierarchical SOM that enables us select number of data to train and label a particular map based on a pre-defined factor and provides consistent hierarchical SOM browsing.
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- IEICE TRANSACTIONS on Information Vol.E86-D No.9 pp.1796-1803
- Publication Date
- 2003/09/01
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- Special Section PAPER (Special Issue on Text Processing for Information Access)
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Junan CHAKMA, Kyoji UMEMURA, "Factor Controlled Hierarchical SOM Visualization for Large Set of Data" in IEICE TRANSACTIONS on Information,
vol. E86-D, no. 9, pp. 1796-1803, September 2003, doi: .
Abstract: Self-organizing map is a widely used tool in high-dimensional data visualization. However, despite its benefits of plotting very high-dimensional data on a low-dimensional grid, browsing and understanding the meaning of a trained map turn to be a difficult task -- specially when number of nodes or the size of data increases. Though there are some well-known techniques to visualize SOMs, they mainly deals with cluster boundaries and they fail to consider raw information available in original data in browsing SOMs. In this paper, we propose our Factor controlled Hierarchical SOM that enables us select number of data to train and label a particular map based on a pre-defined factor and provides consistent hierarchical SOM browsing.
URL: https://global.ieice.org/en_transactions/information/10.1587/e86-d_9_1796/_p
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@ARTICLE{e86-d_9_1796,
author={Junan CHAKMA, Kyoji UMEMURA, },
journal={IEICE TRANSACTIONS on Information},
title={Factor Controlled Hierarchical SOM Visualization for Large Set of Data},
year={2003},
volume={E86-D},
number={9},
pages={1796-1803},
abstract={Self-organizing map is a widely used tool in high-dimensional data visualization. However, despite its benefits of plotting very high-dimensional data on a low-dimensional grid, browsing and understanding the meaning of a trained map turn to be a difficult task -- specially when number of nodes or the size of data increases. Though there are some well-known techniques to visualize SOMs, they mainly deals with cluster boundaries and they fail to consider raw information available in original data in browsing SOMs. In this paper, we propose our Factor controlled Hierarchical SOM that enables us select number of data to train and label a particular map based on a pre-defined factor and provides consistent hierarchical SOM browsing.},
keywords={},
doi={},
ISSN={},
month={September},}
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TY - JOUR
TI - Factor Controlled Hierarchical SOM Visualization for Large Set of Data
T2 - IEICE TRANSACTIONS on Information
SP - 1796
EP - 1803
AU - Junan CHAKMA
AU - Kyoji UMEMURA
PY - 2003
DO -
JO - IEICE TRANSACTIONS on Information
SN -
VL - E86-D
IS - 9
JA - IEICE TRANSACTIONS on Information
Y1 - September 2003
AB - Self-organizing map is a widely used tool in high-dimensional data visualization. However, despite its benefits of plotting very high-dimensional data on a low-dimensional grid, browsing and understanding the meaning of a trained map turn to be a difficult task -- specially when number of nodes or the size of data increases. Though there are some well-known techniques to visualize SOMs, they mainly deals with cluster boundaries and they fail to consider raw information available in original data in browsing SOMs. In this paper, we propose our Factor controlled Hierarchical SOM that enables us select number of data to train and label a particular map based on a pre-defined factor and provides consistent hierarchical SOM browsing.
ER -
English
