Multisolutional clustering and quantization algorithm (MCQ)

I. Dvorchik; W. Marsh; V. Gurari; M. Subotin; H. R. Doyle

doi:10.1016/0010-4825(96)00021-2

Multisolutional clustering and quantization algorithm (MCQ)

I. Dvorchik, W. Marsh, V. Gurari, M. Subotin, H. R. Doyle

Research output: Contribution to journal › Article › peer-review

Abstract

We have developed a novel clustering and quantization algorithm that allows the user to create multiple one-to-one correspondences between the actual data and its transformed (clustered and quantized) values, based on the user's hypothesis regarding the nature of the classification task. The types of problems for which the algorithm can be beneficial are discussed. We report experiments employing simulated and real data that suggest the proposed algorithm may be useful in neural network analysis of various phenomena in medicine and biology.

Original language	English (US)
Pages (from-to)	439-450
Number of pages	12
Journal	Computers in Biology and Medicine
Volume	26
Issue number	5
DOIs	https://doi.org/10.1016/0010-4825(96)00021-2
State	Published - Sep 1996
Externally published	Yes

Keywords

Algorithms
Atrioventricular node
Cluster analysis
Computer
Models
Neural networks
Theoretical
Vector quantization

ASJC Scopus subject areas

Computer Science Applications
Health Informatics

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10.1016/0010-4825(96)00021-2

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abstract = "We have developed a novel clustering and quantization algorithm that allows the user to create multiple one-to-one correspondences between the actual data and its transformed (clustered and quantized) values, based on the user's hypothesis regarding the nature of the classification task. The types of problems for which the algorithm can be beneficial are discussed. We report experiments employing simulated and real data that suggest the proposed algorithm may be useful in neural network analysis of various phenomena in medicine and biology.",

keywords = "Algorithms, Atrioventricular node, Cluster analysis, Computer, Models, Neural networks, Theoretical, Vector quantization",

author = "I. Dvorchik and W. Marsh and V. Gurari and M. Subotin and Doyle, {H. R.}",

year = "1996",

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doi = "10.1016/0010-4825(96)00021-2",

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pages = "439--450",

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AU - Dvorchik, I.

AU - Marsh, W.

AU - Gurari, V.

AU - Subotin, M.

AU - Doyle, H. R.

PY - 1996/9

Y1 - 1996/9

N2 - We have developed a novel clustering and quantization algorithm that allows the user to create multiple one-to-one correspondences between the actual data and its transformed (clustered and quantized) values, based on the user's hypothesis regarding the nature of the classification task. The types of problems for which the algorithm can be beneficial are discussed. We report experiments employing simulated and real data that suggest the proposed algorithm may be useful in neural network analysis of various phenomena in medicine and biology.

AB - We have developed a novel clustering and quantization algorithm that allows the user to create multiple one-to-one correspondences between the actual data and its transformed (clustered and quantized) values, based on the user's hypothesis regarding the nature of the classification task. The types of problems for which the algorithm can be beneficial are discussed. We report experiments employing simulated and real data that suggest the proposed algorithm may be useful in neural network analysis of various phenomena in medicine and biology.

KW - Algorithms

KW - Atrioventricular node

KW - Cluster analysis

KW - Computer

KW - Models

KW - Neural networks

KW - Theoretical

KW - Vector quantization

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JO - Computers in Biology and Medicine

JF - Computers in Biology and Medicine

IS - 5

ER -

Multisolutional clustering and quantization algorithm (MCQ)

Abstract

Keywords

ASJC Scopus subject areas

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