Analysis of Fuzzy and Possibilistic C-Means Clustering Algorithms on Protein Localization with Ecoli Data
Abstract
Clustering
is the process of dividing data clusters into fragmented clusters so that the
same set of data is similar, but the data of different clusters is different.
The basis of the fuzzy clustering algorithms is the c-means families and the
strongest algorithm is the fuzzy c-means algorithm. However, the fuzzy c-means algorithm
is sensitive to outliers. In this study, on the real data set we examined three
different algorithms -possibilistic c-means algorithm (PCM), fuzzy possibilistic
c-means (FPCM) and possibilistic fuzzy c- means algorithm (PFCM)- which are developed to overcome the
unfavorable side of the FCM algorithm. To compare these algorithms, iteration
numbers and completion times were calculated.
Keywords
Fuzzy c-means Possibilistic c-means Fuzzy possibilistic c-means Possibilistic fuzzy c- means
References
- Berry M. W., 2003. Survey of Text Mining, Springer-Verlag, New York, NY, USA
- Berthold M R. and Hand D. J., 1999. Intelligent Data Analysis, Springer-Verlag, Berlin, Germany
- Bezdek J. C., 1981. Pattern Recognition with Fuzzy Objective Function Algorithms, New York: Plenum
- Horton P. and Nakai K., 1996. A Probablistic Classification System for Predicting the Cellular Localization Sites of Proteins, Proc Int Conf Intell Syst Mol Biol. 4:109-15
- Krishnapuram R. and Keller J., 1993. A possibilistic approach to clustering, IEEE Trans. Fuzzy Syst., vol. 1, no. 2, pp. 98-110
- Nakai K. and Kanehisa M., 1992. A Knowledge Base for Predicting Protein Localization Sites in Eukaryotic Cells, , Genomics 14:897-911
- Nakai K. and Kanehisa M., 1991. Expert Sytem for Predicting Protein Localization Sites in Gram-Negative Bacteria,PROTEINS: Structure, Function, and Genetics 11:95-110
- Nefti S. and Oussalah M., 2004. Probabilistic-Fuzzy Clustering Algorithm, IEEE international Conference on Systems, Man and Cybemetics, pp. 4786-4791
- Pal N. R., Pal K., and Bezdek J. C., 1997. A mixed c-means clustering model, in IEEE Int. Conf. Fuzzy Systems, Spain, pp. 11 -21
- Pal N. R., Pal K., Keller J. M., and Bezdek J. C., 2005. A Possibilistic Fuzzy c-Means Clustering Algorithm, IEEE Trans. on Fuzzy Systems, vol. 13, no. 4, pp. 517-530
- Ruspini E. R, 1969. A New Approach to Clustering, Inform. Control, vol. 15, no. 1, pp. 22-32
- Singhal R., Deepika N., 2016. Classification of Words: Using PFCM Clustering, International Journal of Computer Science and Mobile Computing, Vol.5 Issue.4, pg. 114-117
- Timm H, Borgelt C., Doring C., Kruse R., 2004. An extension to possibilistic fuzzy cluster analysis”, Fuzzy Sets and Systems 147 3–16
- Zadeh L., 1965. Fuzzy Sets, Inform. Control, 8, pp. 338-353
- https://archive.ics.uci.edu/ml/index.php
Abstract
Kümeleme, veri kümelerini
parçalanmış kümelere bölme işlemidir, böylece aynı veri kümesi benzerdir,
farklı kümelerin verileri farklıdır. Bulanık kümeleme algoritmalarının temeli
c- ortalamalar aileleridir ve en güçlü algoritma bulanık c- ortalamalar algoritmasıdır.
Bununla birlikte, bulanık c- ortalamalar algoritması aykırı değerlere
duyarlıdır. Bu çalışmada, gerçek veri seti üzerinde, bulanık c- ortalamalar
algoritmasının bu olumsuz etkinin üstesinden gelmek için geliştirilen üç farklı
algoritma – olabilirlikli c-ortalamalar algoritması (PCM), bulanık
olabilirlikli c-ortalamalar (FPCM) ve olabilirlikli bulanık c- ortalamalar
algoritması (PFCM) – incelenmiştir. Bu algoritmaları karşılaştırmak için
yineleme sayıları ve tamamlanma süreleri hesaplanmıştır.
Keywords
Bulanık c- ortalamalar Olabilirlikli c- ortalamalar Bulanık olabilirlikli c- ortalamalar Olabilirlikli bulanık c- ortalamalar
References
- Berry M. W., 2003. Survey of Text Mining, Springer-Verlag, New York, NY, USA
- Berthold M R. and Hand D. J., 1999. Intelligent Data Analysis, Springer-Verlag, Berlin, Germany
- Bezdek J. C., 1981. Pattern Recognition with Fuzzy Objective Function Algorithms, New York: Plenum
- Horton P. and Nakai K., 1996. A Probablistic Classification System for Predicting the Cellular Localization Sites of Proteins, Proc Int Conf Intell Syst Mol Biol. 4:109-15
- Krishnapuram R. and Keller J., 1993. A possibilistic approach to clustering, IEEE Trans. Fuzzy Syst., vol. 1, no. 2, pp. 98-110
- Nakai K. and Kanehisa M., 1992. A Knowledge Base for Predicting Protein Localization Sites in Eukaryotic Cells, , Genomics 14:897-911
- Nakai K. and Kanehisa M., 1991. Expert Sytem for Predicting Protein Localization Sites in Gram-Negative Bacteria,PROTEINS: Structure, Function, and Genetics 11:95-110
- Nefti S. and Oussalah M., 2004. Probabilistic-Fuzzy Clustering Algorithm, IEEE international Conference on Systems, Man and Cybemetics, pp. 4786-4791
- Pal N. R., Pal K., and Bezdek J. C., 1997. A mixed c-means clustering model, in IEEE Int. Conf. Fuzzy Systems, Spain, pp. 11 -21
- Pal N. R., Pal K., Keller J. M., and Bezdek J. C., 2005. A Possibilistic Fuzzy c-Means Clustering Algorithm, IEEE Trans. on Fuzzy Systems, vol. 13, no. 4, pp. 517-530
- Ruspini E. R, 1969. A New Approach to Clustering, Inform. Control, vol. 15, no. 1, pp. 22-32
- Singhal R., Deepika N., 2016. Classification of Words: Using PFCM Clustering, International Journal of Computer Science and Mobile Computing, Vol.5 Issue.4, pg. 114-117
- Timm H, Borgelt C., Doring C., Kruse R., 2004. An extension to possibilistic fuzzy cluster analysis”, Fuzzy Sets and Systems 147 3–16
- Zadeh L., 1965. Fuzzy Sets, Inform. Control, 8, pp. 338-353
- https://archive.ics.uci.edu/ml/index.php
Details
| Primary Language | English |
|---|---|
| Subjects | Engineering |
| Journal Section | Articles |
| Authors | |
| Publication Date | May 28, 2019 |
| Submission Date | June 1, 2018 |
| Published in Issue | Year 2019 Volume: 19 Issue: 1 |