A fuzzy c-means clustering algorithm is presented which is much faster than the traditional algorithm for data sets in which the number of features is significantly larger than the number of feature vectors. The algorithm is constructed by utilizing the covariance structure of feature vectors and cl
Fuzzy c-means clustering methods for symbolic interval data
β Scribed by Francisco de A.T. de Carvalho
- Publisher
- Elsevier Science
- Year
- 2007
- Tongue
- English
- Weight
- 449 KB
- Volume
- 28
- Category
- Article
- ISSN
- 0167-8655
No coin nor oath required. For personal study only.
π SIMILAR VOLUMES
A conventional FCM algorithm does not fully utilize the spatial information in the image. In this paper, we present a fuzzy c-means (FCM) algorithm that incorporates spatial information into the membership function for clustering. The spatial function is the summation of the membership function in t
The Fuzzy C-Means Algorithm (FCMA) was applied for the discrimination between seed species by artificial vision. Colour images of seeds belonging to 4 species were acquired with a CCD camera. In order to characterise the morphology of the seeds, a set of quantitative features were extracted from the