Methods of cluster analysis in sensor engineering: advantages and faults
We consider the crisp and fuzzy partitioning techniques of cluster analysis bearing in mind their application for classification of data obtained with chemical sensor arrays. The advantage of the cluster analysis techniques is existence of a parameter S(i). This parameter gives quantitative effic...
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| Published in: | Semiconductor Physics Quantum Electronics & Optoelectronics |
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| Date: | 2010 |
| Main Authors: | , |
| Format: | Article |
| Language: | English |
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Інститут фізики напівпровідників імені В.Є. Лашкарьова НАН України
2010
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| Online Access: | https://nasplib.isofts.kiev.ua/handle/123456789/118565 |
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| Journal Title: | Digital Library of Periodicals of National Academy of Sciences of Ukraine |
| Cite this: | Methods of cluster analysis in sensor engineering: advantages and faults / Yu.V. Burlachenko, B.A. Snopok // Semiconductor Physics Quantum Electronics & Optoelectronics. — 2010. — Т. 13, № 4. — С. 393-397. — Бібліогр.: 13 назв. — англ. |
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Digital Library of Periodicals of National Academy of Sciences of Ukraine| id |
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Burlachenko, Yu.V. Snopok, B.A. 2017-05-30T16:19:45Z 2017-05-30T16:19:45Z 2010 Methods of cluster analysis in sensor engineering: advantages and faults / Yu.V. Burlachenko, B.A. Snopok // Semiconductor Physics Quantum Electronics & Optoelectronics. — 2010. — Т. 13, № 4. — С. 393-397. — Бібліогр.: 13 назв. — англ. 1560-8034 PACS 07.07.Df https://nasplib.isofts.kiev.ua/handle/123456789/118565 We consider the crisp and fuzzy partitioning techniques of cluster analysis bearing in mind their application for classification of data obtained with chemical sensor arrays. The advantage of the cluster analysis techniques is existence of a parameter S(i). This parameter gives quantitative efficiency of classification and can be used as optimization criterion for sensor system as a whole as well as the measurement procedure. The crisp and fuzzy techniques give practically the same result when analyzing the data that cluster uniquely. It is shown that big value of the parameter S(i) is not sufficient for adequate data partitioning into cluster in more complicated cases, and the results of clusterization for the above techniques may diverge. In this case, one should apply both techniques concurrently, checking the correctness of partitioning into clusters against the principal component analysis. This work got a financial support from the National Academy of Sciences of Ukraine. en Інститут фізики напівпровідників імені В.Є. Лашкарьова НАН України Semiconductor Physics Quantum Electronics & Optoelectronics Methods of cluster analysis in sensor engineering: advantages and faults Article published earlier |
| institution |
Digital Library of Periodicals of National Academy of Sciences of Ukraine |
| collection |
DSpace DC |
| title |
Methods of cluster analysis in sensor engineering: advantages and faults |
| spellingShingle |
Methods of cluster analysis in sensor engineering: advantages and faults Burlachenko, Yu.V. Snopok, B.A. |
| title_short |
Methods of cluster analysis in sensor engineering: advantages and faults |
| title_full |
Methods of cluster analysis in sensor engineering: advantages and faults |
| title_fullStr |
Methods of cluster analysis in sensor engineering: advantages and faults |
| title_full_unstemmed |
Methods of cluster analysis in sensor engineering: advantages and faults |
| title_sort |
methods of cluster analysis in sensor engineering: advantages and faults |
| author |
Burlachenko, Yu.V. Snopok, B.A. |
| author_facet |
Burlachenko, Yu.V. Snopok, B.A. |
| publishDate |
2010 |
| language |
English |
| container_title |
Semiconductor Physics Quantum Electronics & Optoelectronics |
| publisher |
Інститут фізики напівпровідників імені В.Є. Лашкарьова НАН України |
| format |
Article |
| description |
We consider the crisp and fuzzy partitioning techniques of cluster analysis
bearing in mind their application for classification of data obtained with chemical sensor
arrays. The advantage of the cluster analysis techniques is existence of a parameter S(i).
This parameter gives quantitative efficiency of classification and can be used as
optimization criterion for sensor system as a whole as well as the measurement
procedure. The crisp and fuzzy techniques give practically the same result when
analyzing the data that cluster uniquely. It is shown that big value of the parameter S(i) is
not sufficient for adequate data partitioning into cluster in more complicated cases, and
the results of clusterization for the above techniques may diverge. In this case, one
should apply both techniques concurrently, checking the correctness of partitioning into
clusters against the principal component analysis.
|
| issn |
1560-8034 |
| url |
https://nasplib.isofts.kiev.ua/handle/123456789/118565 |
| citation_txt |
Methods of cluster analysis in sensor engineering: advantages and faults / Yu.V. Burlachenko, B.A. Snopok // Semiconductor Physics Quantum Electronics & Optoelectronics. — 2010. — Т. 13, № 4. — С. 393-397. — Бібліогр.: 13 назв. — англ. |
| work_keys_str_mv |
AT burlachenkoyuv methodsofclusteranalysisinsensorengineeringadvantagesandfaults AT snopokba methodsofclusteranalysisinsensorengineeringadvantagesandfaults |
| first_indexed |
2025-11-28T12:05:05Z |
| last_indexed |
2025-11-28T12:05:05Z |
| _version_ |
1850853732063903744 |