IEICE TRANSACTIONS on Fundamentals
Multi-clustering Network for Data Classification System
Summary :
This paper presents a new multi-clustering network for the purpose of intelligent data classification. In this network, the first layer is a self-organized clustering layer and the second layer is a restricted clustering layer with a neighborhood mechanism. A new clustering algorithm is developed in this system for the efficiently use of parallel processors. This parallel algorithm enables the nodes of this network to be independently processed in order to minimize data communication load among processors. Using the parallel processors, the quite low calculation cost can be realized among the conventional networks. For example, a 4-processor parallel computing system has shown its ability to reduce the time taken for data classification to 26.75% of a single processor system without declining its performance.
- Publication
- IEICE TRANSACTIONS on Fundamentals Vol.E80-A No.9 pp.1647-1654
- Publication Date
- 1997/09/25
- Publicized
- Online ISSN
- DOI
- Type of Manuscript
- Category
- Digital Signal Processing
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Rafiqul ISLAM, Yoshikazu MIYANAGA, Koji TOCHINAI, "Multi-clustering Network for Data Classification System" in IEICE TRANSACTIONS on Fundamentals,
vol. E80-A, no. 9, pp. 1647-1654, September 1997, doi: .
Abstract: This paper presents a new multi-clustering network for the purpose of intelligent data classification. In this network, the first layer is a self-organized clustering layer and the second layer is a restricted clustering layer with a neighborhood mechanism. A new clustering algorithm is developed in this system for the efficiently use of parallel processors. This parallel algorithm enables the nodes of this network to be independently processed in order to minimize data communication load among processors. Using the parallel processors, the quite low calculation cost can be realized among the conventional networks. For example, a 4-processor parallel computing system has shown its ability to reduce the time taken for data classification to 26.75% of a single processor system without declining its performance.
URL: https://global.ieice.org/en_transactions/fundamentals/10.1587/e80-a_9_1647/_p
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@ARTICLE{e80-a_9_1647,
author={Rafiqul ISLAM, Yoshikazu MIYANAGA, Koji TOCHINAI, },
journal={IEICE TRANSACTIONS on Fundamentals},
title={Multi-clustering Network for Data Classification System},
year={1997},
volume={E80-A},
number={9},
pages={1647-1654},
abstract={This paper presents a new multi-clustering network for the purpose of intelligent data classification. In this network, the first layer is a self-organized clustering layer and the second layer is a restricted clustering layer with a neighborhood mechanism. A new clustering algorithm is developed in this system for the efficiently use of parallel processors. This parallel algorithm enables the nodes of this network to be independently processed in order to minimize data communication load among processors. Using the parallel processors, the quite low calculation cost can be realized among the conventional networks. For example, a 4-processor parallel computing system has shown its ability to reduce the time taken for data classification to 26.75% of a single processor system without declining its performance.},
keywords={},
doi={},
ISSN={},
month={September},}
Copy
TY - JOUR
TI - Multi-clustering Network for Data Classification System
T2 - IEICE TRANSACTIONS on Fundamentals
SP - 1647
EP - 1654
AU - Rafiqul ISLAM
AU - Yoshikazu MIYANAGA
AU - Koji TOCHINAI
PY - 1997
DO -
JO - IEICE TRANSACTIONS on Fundamentals
SN -
VL - E80-A
IS - 9
JA - IEICE TRANSACTIONS on Fundamentals
Y1 - September 1997
AB - This paper presents a new multi-clustering network for the purpose of intelligent data classification. In this network, the first layer is a self-organized clustering layer and the second layer is a restricted clustering layer with a neighborhood mechanism. A new clustering algorithm is developed in this system for the efficiently use of parallel processors. This parallel algorithm enables the nodes of this network to be independently processed in order to minimize data communication load among processors. Using the parallel processors, the quite low calculation cost can be realized among the conventional networks. For example, a 4-processor parallel computing system has shown its ability to reduce the time taken for data classification to 26.75% of a single processor system without declining its performance.
ER -
English
