SavedAdd your journal

Journals / Afyon Kocatepe Üniversitesi Fen ve Mühendislik Bilimleri Dergisi / 2019 / Cilt: 19 - Sayı: 1

Analysis of Fuzzy and Possibilistic C-Means Clustering Algorithms on Protein Localization with Ecoli Data

Ecoli Veri Protein Lokalizasyonunda Bulanık ve Olabilirlikli Kümeleme Algoritmalarının Analizi

Ozer OZDEMİR
Aslı KAYA
Pages
92–102
Publication Date
2019-05-28
DOI
—
🏛️Bibliographic metadata and full text access for this article are provided via DergiPark.
DergiPark Record ↗
✨

AI Summary & Key Takeaways

AI Insight
⚡ TL;DR(Too Long; Didn't Read)

To compare these algorithms, iteration numbers and completion times were calculated.

📌 3 Key Takeaways

🎯
Objective & Hypothesis: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.
📊
Key Findings & Data:The basis of the fuzzy clustering algorithms is the c-means families and the strongest algorithm is the fuzzy c-means algorithm.
💡
Conclusion & Impact:To compare these algorithms, iteration numbers and completion times were calculated.
🔬Design:Research Article
📍Scope:Comprehensive Sample
⚙️Variables:Bulanık c- ortalamalar · Olabilirlikli c- ortalamalar · Bulanık olabilirlikli c- ortalamalar

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.

Özet

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.

🔖

Cite This Article

OZDEMİR, O., KAYA, A. (2019). Analysis of Fuzzy and Possibilistic C-Means Clustering Algorithms on Protein Localization with Ecoli Data. Afyon Kocatepe Üniversitesi Fen ve Mühendislik Bilimleri Dergisi, Cilt: 19 - Sayı: 1, 92–102. https://ajindex.com/articles/451464

📄 Full Text PDF Viewer

📄

PDF görüntüleyici hazırlanıyor...

References (8)

  1. Berry M. W., 2003.
  2. Survey of Text Mining, Springer-Verlag, New York, NY, USA Berthold M R. and Hand D. J., 1999.
  3. 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
  4. 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.
  5. A New Approach to Clustering, Inform. Control, vol. 15, no. 1, pp. 22-32
  6. 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
  7. 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.
  8. Fuzzy Sets, Inform. Control, 8, pp. 338-353 https://archive.ics.uci.edu/ml/index.php