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...
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| Опубліковано в: : | Semiconductor Physics Quantum Electronics & Optoelectronics |
|---|---|
| Дата: | 2010 |
| Автори: | , |
| Формат: | Стаття |
| Мова: | Англійська |
| Опубліковано: |
Інститут фізики напівпровідників імені В.Є. Лашкарьова НАН України
2010
|
| Онлайн доступ: | https://nasplib.isofts.kiev.ua/handle/123456789/118565 |
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| Назва журналу: | Digital Library of Periodicals of National Academy of Sciences of Ukraine |
| Цитувати: | 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. |
Репозитарії
Digital Library of Periodicals of National Academy of Sciences of Ukraine| _version_ | 1862605598682513408 |
|---|---|
| author | Burlachenko, Yu.V. Snopok, B.A. |
| author_facet | Burlachenko, Yu.V. Snopok, B.A. |
| 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. |
| collection | DSpace DC |
| container_title | Semiconductor Physics Quantum Electronics & Optoelectronics |
| 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.
|
| first_indexed | 2025-11-28T12:05:05Z |
| format | Article |
| fulltext | |
| id | nasplib_isofts_kiev_ua-123456789-118565 |
| institution | Digital Library of Periodicals of National Academy of Sciences of Ukraine |
| issn | 1560-8034 |
| language | English |
| last_indexed | 2025-11-28T12:05:05Z |
| publishDate | 2010 |
| publisher | Інститут фізики напівпровідників імені В.Є. Лашкарьова НАН України |
| record_format | dspace |
| spelling | 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 |
| spellingShingle | Methods of cluster analysis in sensor engineering: advantages and faults Burlachenko, Yu.V. Snopok, B.A. |
| title | 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_short | Methods of cluster analysis in sensor engineering: advantages and faults |
| title_sort | methods of cluster analysis in sensor engineering: advantages and faults |
| url | https://nasplib.isofts.kiev.ua/handle/123456789/118565 |
| work_keys_str_mv | AT burlachenkoyuv methodsofclusteranalysisinsensorengineeringadvantagesandfaults AT snopokba methodsofclusteranalysisinsensorengineeringadvantagesandfaults |