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...
Збережено в:
Дата: | 2010 |
---|---|
Автори: | , |
Формат: | Стаття |
Мова: | English |
Опубліковано: |
Інститут фізики напівпровідників імені В.Є. Лашкарьова НАН України
2010
|
Назва видання: | Semiconductor Physics Quantum Electronics & Optoelectronics |
Онлайн доступ: | http://dspace.nbuv.gov.ua/handle/123456789/118565 |
Теги: |
Додати тег
Немає тегів, Будьте першим, хто поставить тег для цього запису!
|
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 UkraineРезюме: | 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. |
---|