Pattern Recognition in Numerical Data Sets and Color Images through the Typicality Based on the GKPFCM Clustering Algorithm
Joint Authors
Ojeda-Magaña, B.
Ruelas, R.
Corona Nakamura, M. A.
Carr Finch, D. W.
Gómez-Barba, L.
Source
Mathematical Problems in Engineering
Issue
Vol. 2013, Issue 2013 (31 Dec. 2013), pp.1-11, 11 p.
Publisher
Hindawi Publishing Corporation
Publication Date
2013-12-14
Country of Publication
Egypt
No. of Pages
11
Main Subjects
Abstract EN
We take the concept of typicality from the field of cognitive psychology, and we apply the meaning to the interpretation of numerical data sets and color images through fuzzy clustering algorithms, particularly the GKPFCM, looking to get better information from the processed data.
The Gustafson Kessel Possibilistic Fuzzy c-means (GKPFCM) is a hybrid algorithm that is based on a relative typicality (membership degree, Fuzzy c-means) and an absolute typicality (typicality value, Possibilistic c-means).
Thus, using both typicalities makes it possible to learn and analyze data as well as to relate the results with the theory of prototypes.
In order to demonstrate these results we use a synthetic data set and a digitized image of a glass, in a first example, and images from the Berkley database, in a second example.
The results clearly demonstrate the advantages of the information obtained about numerical data sets, taking into account the different meaning of typicalities and the availability of both values with the clustering algorithm used.
This approach allows the identification of small homogeneous regions, which are difficult to find.
American Psychological Association (APA)
Ojeda-Magaña, B.& Ruelas, R.& Corona Nakamura, M. A.& Carr Finch, D. W.& Gómez-Barba, L.. 2013. Pattern Recognition in Numerical Data Sets and Color Images through the Typicality Based on the GKPFCM Clustering Algorithm. Mathematical Problems in Engineering،Vol. 2013, no. 2013, pp.1-11.
https://search.emarefa.net/detail/BIM-1010482
Modern Language Association (MLA)
Ojeda-Magaña, B.…[et al.]. Pattern Recognition in Numerical Data Sets and Color Images through the Typicality Based on the GKPFCM Clustering Algorithm. Mathematical Problems in Engineering No. 2013 (2013), pp.1-11.
https://search.emarefa.net/detail/BIM-1010482
American Medical Association (AMA)
Ojeda-Magaña, B.& Ruelas, R.& Corona Nakamura, M. A.& Carr Finch, D. W.& Gómez-Barba, L.. Pattern Recognition in Numerical Data Sets and Color Images through the Typicality Based on the GKPFCM Clustering Algorithm. Mathematical Problems in Engineering. 2013. Vol. 2013, no. 2013, pp.1-11.
https://search.emarefa.net/detail/BIM-1010482
Data Type
Journal Articles
Language
English
Notes
Includes bibliographical references
Record ID
BIM-1010482