fcvalid: An R Package for Internal Validation of Probabilistic and Possibilistic Clustering

Zeynel Cebeci (Çukurova Üniversitesi, Biyometri ve Genetik Bölümü, Adana, Türkiye)
Sakarya University Journal of Computer and Information Sciences (Online)
2 0
Yıl: 2020 Cilt: 3 Sayı: 1 Sayfa Aralığı: 11 - 27 Metin Dili: İngilizce DOI: 10.35377/saucis.03.01.664560 İndeks Tarihi: 12-10-2020

fcvalid: An R Package for Internal Validation of Probabilistic and Possibilistic Clustering

Öz:
In exploratory data analysis and machine learning, partitioning clustering is a frequently used unsupervisedlearning technique for finding the meaningful patterns in numeric datasets. Clustering aims to identify and classifythe objects or the cases in datasets in practice. The clustering quality or the performance of a clustering algorithmis generally evaluated by using the internal validity indices. In this study, an R package named 'fcvalid' isintroduced for validation of fuzzy and possibilistic clustering results. The package implements a broad collectionof the internal indices which have been proposed to validate the results of fuzzy clustering algorithms.Additionally, the options to compute the generalized and extended versions of the fuzzy internal indices forvalidation of the possibilistic clustering are also included in the package.
Anahtar Kelime:

fcvalid: Olasılıklı ve Olabilirlikli Bölümleyici Kümelemede Bulanık Geçerlilik İndeksleri için Bir R Paketi

Öz:
Bölümleyici kümeleme, keşifsel veri analizi ve makine öğrenmesinde sayısal veri kümelerindeki anlamlı örüntüleri bulmak için yaygın olarak kullanılan denetimsiz öğrenme tekniklerinden biridir. Kümeleme, pratikte veri kümesindeki nesneleri veya olguları tanımayı ve sınıflandırmayı amaçlar. Bir kümeleme analizinin kalitesi veya bir kümeleme algoritmasının performansı genellikle iç geçerlilik endeksleri kullanılarak değerlendirilir. Bu çalışmada, bulanık ve olabilirlikli kümeleme sonuçlarının doğrulanması için 'fcvalid' adında bir R paketinin işlevleri tanıtılmaktadır. Paket, bulanık kümeleme algoritmalarının sonuçlarını doğrulamak için önerilen çok sayıda iç endeksin uygulamasını içermektedir. Ayrıca, olabilirlikli kümelemenin doğrulanması için bulanık iç endekslerin genelleştirilmiş ve genişletilmiş sürümlerini hesaplama seçenekleri de pakete dâhil edilmiştir.
Anahtar Kelime:

Belge Türü: Makale Makale Türü: Araştırma Makalesi Erişim Türü: Erişime Açık
  • [1] J.C. Bezdek, Pattern Recognition with Fuzzy Objective Function Algorithms. Plenum Press, New York, 1981.
  • [2] R. Krishnapuram, J. Keller, “A possibilistic approach to clustering”, IEEE Transactions on Fuzzy Systems, vol. 1, pp. 98-110, 1993.
  • [3] R. Krishnapuram, J. Keller, “The possibilistic c-means algorithm: Insights and recommendations”, IEEE Transactions on Fuzzy Systems, vol. 4, pp. 385-393, 1996.
  • [4] N.R. Pal, K. Pal, J.C. Bezdek, “A mixed c-means clustering model”, Proc. of the 6th IEEE Int. Conf. on Fuzzy Systems, vol. 1, pp. 11-21, 1997.
  • [5] N.R. Pal, K. Pal, J.M. Keller, J.C. Bezdek, “A possibilistic fuzzy c-means clustering algorithm”, IEEE Transactions on Fuzzy Systems, vol. 13, no. 4, pp. 517-530, 2005.
  • [6] K.L. Wu, M.S. Yang, “A cluster validity index for fuzzy clustering”, Pattern Recognition Letters, vol. 26, no. 9, pp. 1275-1291, 2005.
  • [7] X. Wu, B. Wu, J. Sun, H. Fu, “Unsupervised possibilistic fuzzy clustering”, J of Information & Computational Science, vol. 7, no. 5, pp. 1075-1080, 2010.
  • [8] M. R. Rezaee, B. P. Lelieveldt, J. H. Reiber, “A new cluster validity index for the fuzzy c-mean”, Pattern Recognition Letters, vol. 19, no. 3, pp. 237-246, 1998.
  • [9] M. Halkidi, Y. Batistakis, M. Vazirgiannis, “On clustering validation techniques”, J of Intelligent Information Systems, vol. 17, no. 2–3, pp. 107-145, 2001.
  • [10] M. Halkidi, Y. Batistakis, M. Vazirgiannis, “Cluster validity methods: Part I”, ACM Sigmod Record, vol. 31, no. 2, pp. 40-45, 2002.
  • [11] M. Halkidi, Y. Batistakis, M. Vazirgiannis, “Cluster validity methods: Part II”, ACM Sigmod Record, vol. 31, no. 3, pp. 19-27, 2002.
  • [12] A.K. Jain, & R.C. Dubes. Algorithms for Clustering Data. Englewood Cliffs: Prentice Hall, 1988.
  • [13] J.C. Bezdek, “Cluster validity with fuzzy sets”, J Cybernetics, vol. 3, no. 3, pp. 58-72, 1974.
  • [14] I. Gath, A.B. Geva, “Unsupervised optimal fuzzy clustering”, IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 11, no. 7, pp. 773-780, 1989.
  • [15] Y. Fukuyama, M. Sugeno, “A new method of choosing the number of cluster for the fuzzy cmeans method”, Proc. of the 5th Fuzzy Systems Symp., pp. 247-250, 1989.
  • [16] X.L. Xie, G. Beni, “A validity measure for fuzzy clustering”, IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 13, no. 8, pp. 841-847, 1991.
  • [17] R. Krishnapuram, C-P. Freg, “Fitting an unknown number of lines and planes to image data through compatible cluster merging”, Pattern Recognition, vol. 25, pp. 385-400, 1992.
  • [18] S.H. Kwon, “Cluster validity index for fuzzy clustering”, Electronics Letters, vol. 34, no. 22, pp. 2176-2177, 1998.
  • [19] A.M. Bensaid, L.O. Hall, J.C. Bezdek, L.P. Clarke, M.L. Silbiger, J.A. Arrington, R.F. Murtagh, “Validity-guided (re)clustering with applications to image segmentation”, IEEE Transactions on Fuzzy Systems, vol. 4, no.2, pp. 112-123, 1996.
  • [20] R.N. Dave, “Validating fuzzy partitions obtained through c-shells clustering”, Pattern Recognition Letters, vol. 17, pp. 613-623, 1996.
  • [21] M.R. Rezaee, B.P. Lelieveldt, J.H. Reiber, “A new cluster validity index for the fuzzy c-mean”, Pattern Recognition Letters, vol. 19, no. 3, pp. 237-246, 1998.
  • [22] N. Zahid, M. Limouri, A. Essaid, “A new cluster-validity for fuzzy clustering”, Pattern Recognition, vol. 32, no. 7, pp. 1089-1097, 1999.
  • [23] M.Y. Chen, D.A. Linkens, “Rule-base self-generation and simplification for data-driven fuzzy models”, Fuzzy Sets and Systems, vol. 142, no. 2, pp. 243-265, 2004. [24] M.K. Pakhira, S. Bandyopadhyay, U. Maulik, “Validity index for crisp and fuzzy clusters”,
  • Pattern Recognition, vol. 37, no. 3, pp. 487-501, 2004.
  • [25] Y. Tang, F. Sun, Z. Sun, “Improved validation index for fuzzy clustering”, Proc. - The American Control Conference,IEEE, pp. 1120-1125, 2005.
  • [26] R.J.G.B. Campello, E.R. Hruschka, “A fuzzy extension of the silhouette width criterion for cluster analysis”, Fuzzy Sets and Systems, vol. 157, no. 21, pp. 2858-2875, 2006.
  • [27] V. Schwaemmle, O.N. Jensen, “A simple and fast method to determine the parameters for fuzzy c-means cluster validation”, 2010. [Online]. Available: http://arxiv.org/abs/1004.1307v1. [Accessed: 24-Dec-2019].
  • [28] A. Chakrabarty, An Investigation of Clustering Algorithms and Soft Computing Approaches for Pattern Recognition. PhD. Thesis, Assam Univ., India, 116 p., 2010. [Online]. Available: http://shodhganga.inflibnet.ac.in/bitstream/10603/93443/16/16_chapter%208.pdf [Accessed: 24- Dec-2019].
  • [29] Z. Cebeci, A.T. Kavlak, F. Yildiz, “Validation of fuzzy and possibilistic clustering results,” Proc. - International Artificial Intelligence and Data Processing Symposium IDAP 2017, IEEE, pp. 1-7, 2017. doi: 10.1109/IDAP.2017.8090183
  • [30] H. Wickham, J. Hester, W. Chang, “devtools: Tools to Make Developing R Packages Easier”, R package version 2.2.1, 2019. [Online]. Available: https://CRAN.Rproject.org/package=devtools, [Accessed: 24-Dec-2019].
  • [31] R. Core Team, “R: A language and environment for statistical computing”, R Foundation for Statistical Computing Vienna Austria, 2017.
  • [32] Y. Xie, J.J. Allaire, G. Grolemund, G., R Markdown: The Definitive Guide. Chapman and Hall/CRC, 2018. [Online]. Available: https://bookdown.org/yihui/rmarkdown, [Accessed: 24- Dec-2019].
  • [33] E. Anderson, “The Irises of the Gaspé Peninsula”, Bull. Amer. Iris Soc., vol. 59, pp. 2-5, 1935.
  • [34] Z. Cebeci, F. Yildiz, A.T. Kavlak, C. Cebeci, H. Onder, “ppclust: Probabilistic and Possibilistic Cluster Analysis”, R package version 0.1.3, 2019. [Online]. Available: https://CRAN.R-project.org/package=ppclust, [Accessed: 24-Dec-2019].
APA Cebeci Z (2020). fcvalid: An R Package for Internal Validation of Probabilistic and Possibilistic Clustering. , 11 - 27. 10.35377/saucis.03.01.664560
Chicago Cebeci Zeynel fcvalid: An R Package for Internal Validation of Probabilistic and Possibilistic Clustering. (2020): 11 - 27. 10.35377/saucis.03.01.664560
MLA Cebeci Zeynel fcvalid: An R Package for Internal Validation of Probabilistic and Possibilistic Clustering. , 2020, ss.11 - 27. 10.35377/saucis.03.01.664560
AMA Cebeci Z fcvalid: An R Package for Internal Validation of Probabilistic and Possibilistic Clustering. . 2020; 11 - 27. 10.35377/saucis.03.01.664560
Vancouver Cebeci Z fcvalid: An R Package for Internal Validation of Probabilistic and Possibilistic Clustering. . 2020; 11 - 27. 10.35377/saucis.03.01.664560
IEEE Cebeci Z "fcvalid: An R Package for Internal Validation of Probabilistic and Possibilistic Clustering." , ss.11 - 27, 2020. 10.35377/saucis.03.01.664560
ISNAD Cebeci, Zeynel. "fcvalid: An R Package for Internal Validation of Probabilistic and Possibilistic Clustering". (2020), 11-27. https://doi.org/10.35377/saucis.03.01.664560
APA Cebeci Z (2020). fcvalid: An R Package for Internal Validation of Probabilistic and Possibilistic Clustering. Sakarya University Journal of Computer and Information Sciences (Online), 3(1), 11 - 27. 10.35377/saucis.03.01.664560
Chicago Cebeci Zeynel fcvalid: An R Package for Internal Validation of Probabilistic and Possibilistic Clustering. Sakarya University Journal of Computer and Information Sciences (Online) 3, no.1 (2020): 11 - 27. 10.35377/saucis.03.01.664560
MLA Cebeci Zeynel fcvalid: An R Package for Internal Validation of Probabilistic and Possibilistic Clustering. Sakarya University Journal of Computer and Information Sciences (Online), vol.3, no.1, 2020, ss.11 - 27. 10.35377/saucis.03.01.664560
AMA Cebeci Z fcvalid: An R Package for Internal Validation of Probabilistic and Possibilistic Clustering. Sakarya University Journal of Computer and Information Sciences (Online). 2020; 3(1): 11 - 27. 10.35377/saucis.03.01.664560
Vancouver Cebeci Z fcvalid: An R Package for Internal Validation of Probabilistic and Possibilistic Clustering. Sakarya University Journal of Computer and Information Sciences (Online). 2020; 3(1): 11 - 27. 10.35377/saucis.03.01.664560
IEEE Cebeci Z "fcvalid: An R Package for Internal Validation of Probabilistic and Possibilistic Clustering." Sakarya University Journal of Computer and Information Sciences (Online), 3, ss.11 - 27, 2020. 10.35377/saucis.03.01.664560
ISNAD Cebeci, Zeynel. "fcvalid: An R Package for Internal Validation of Probabilistic and Possibilistic Clustering". Sakarya University Journal of Computer and Information Sciences (Online) 3/1 (2020), 11-27. https://doi.org/10.35377/saucis.03.01.664560
Sayfa Oluşturulma Tarihi
23-04-2024 13:48

İLETİŞİM

TÜBİTAK ULAKBİM TR Dizin

Yüzüncüyıl, İşçi Blokları Mahallesi

Muhsin Yazıcıoğlu Caddesi No:51/C

06530 Çankaya / ANKARA +90 (312) 298 92 00

trdizin@tubitak.gov.tr

TÜBİTAK ULAKBİM Ulusal Akademik Ağ ve Bilgi Merkezi Cahit Arf Bilgi Merkezi © Tüm Hakları Saklıdır. Gizlilik Politikası Kullanım Şartları