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

Zeynel Cebeci

doi:10.35377/saucis.03.01.664560

TR EN

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

Abstract

In exploratory data analysis and machine learning, partitioning clustering is a frequently used unsupervised learning technique for finding the meaningful patterns in numeric datasets. Clustering aims to identify and classify the objects or the cases in datasets in practice. The clustering quality or the performance of a clustering algorithm is generally evaluated by using the internal validity indices. In this study, an R package named 'fcvalid' is introduced for validation of fuzzy and possibilistic clustering results. The package implements a broad collection of 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 for validation of the possibilistic clustering are also included in the package.

Keywords

internal validity indices,fuzzy clustering,possibilistic clustering,data analysis,R

Supporting Institution

The Unit of Scientific Research Projects of Çukurova University

Project Number

FBA-2019-10285

Thanks

Supplementary materials including the manual and codes of the package 'fcvalid' can be downloaded from GitHub at https://github.com/zcebeci/fcvalid.

References

[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 c-means 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.R-project.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].

Details

Primary Language

English

Subjects

Software Engineering (Other)

Journal Section

Research Article

Authors

Zeynel Cebeci ^*
0000-0002-7641-7094
Türkiye

Publication Date

April 30, 2020

Submission Date

December 24, 2019

Acceptance Date

April 14, 2020

Published in Issue

Year 2020 Volume: 3 Number: 1

DOI

https://doi.org/10.35377/saucis.03.01.664560

IZ

https://izlik.org/JA82FU44MU

Cite

RIS / Bibtex

APA

Cebeci, Z. (2020). fcvalid: An R Package for Internal Validation of Probabilistic and Possibilistic Clustering. Sakarya University Journal of Computer and Information Sciences, 3(1), 11-27. https://doi.org/10.35377/saucis.03.01.664560

AMA

1.Cebeci Z. fcvalid: An R Package for Internal Validation of Probabilistic and Possibilistic Clustering. SAUCIS. 2020;3(1):11-27. doi:10.35377/saucis.03.01.664560

Chicago

Cebeci, Zeynel. 2020. “Fcvalid: An R Package for Internal Validation of Probabilistic and Possibilistic Clustering”. Sakarya University Journal of Computer and Information Sciences 3 (1): 11-27. https://doi.org/10.35377/saucis.03.01.664560.

EndNote

Cebeci Z (April 1, 2020) fcvalid: An R Package for Internal Validation of Probabilistic and Possibilistic Clustering. Sakarya University Journal of Computer and Information Sciences 3 1 11–27.

IEEE

[1]Z. Cebeci, “fcvalid: An R Package for Internal Validation of Probabilistic and Possibilistic Clustering”, SAUCIS, vol. 3, no. 1, pp. 11–27, Apr. 2020, doi: 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 3/1 (April 1, 2020): 11-27. https://doi.org/10.35377/saucis.03.01.664560.

JAMA

1.Cebeci Z. fcvalid: An R Package for Internal Validation of Probabilistic and Possibilistic Clustering. SAUCIS. 2020;3:11–27.

MLA

Cebeci, Zeynel. “Fcvalid: An R Package for Internal Validation of Probabilistic and Possibilistic Clustering”. Sakarya University Journal of Computer and Information Sciences, vol. 3, no. 1, Apr. 2020, pp. 11-27, doi:10.35377/saucis.03.01.664560.

Vancouver

1.Zeynel Cebeci. fcvalid: An R Package for Internal Validation of Probabilistic and Possibilistic Clustering. SAUCIS. 2020 Apr. 1;3(1):11-27. doi:10.35377/saucis.03.01.664560