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Neural Computing and Applications

Erschienen in:

25.01.2019 | Review Article

An improved approach to fuzzy clustering based on FCM algorithm and extended VIKOR method

verfasst von: Hoda Khanali, Babak Vaziri

Erschienen in: Neural Computing and Applications | Ausgabe 2/2020

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Abstract

Fuzzy C-means algorithm is a fuzzy partitional clustering algorithm. However, accuracy and easy to implement have converted this algorithm to the focus of research, and sensitivity to noisy data is an important and challenging issue in the algorithm, so that in recent years, many studies have been done to improve it. In this paper, a clustering algorithm named Fuzzy VIKOR C-means presented that by utilizing the extended VIKOR method based on targeted displacements in the centroids of the clusters seek to benefit from the flexibility property. Moreover, this algorithm also, considering Dunn’s index, means, and density measures as profit criteria, and DB index and the entropy measures as cost criteria, can reduce the sensitivity to noisy data and can enhance performance and quality of clusters. According to the simulation results and comparison with some recent well-known methods, this approach has an effective role in improving the assessment criteria.

Vorheriger Artikel Neural network applications in fault diagnosis and detection: an overview of implementations in engineering-related systems

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Huang T, Hsu W (2013) Conjecturable knowledge discovery: a fuzzy clustering approach. Elsevier Trans Fuzzy Sets Syst 221:1–23MathSciNetCrossRef

Zadeh L (1965) Fuzzy sets. Elsevier Trans Inf Control 8(3):338–353CrossRef

Suganya R, Shanthi R (2012) Fuzzy C- means algorithm—a review. Int J Sci Res Publ 2(11):1–3

Izakian H, Abraham A (2011) Fuzzy C-means and fuzzy swarm for fuzzy clustering problem. Elsevier Trans Expert Syst Appl 38(3):1835–1838CrossRef

Dunn J (1974) A fuzzy relative of the ISODATA process and its use in detecting compact well separated clusters. J Cybern 3(3):32–57MathSciNetCrossRef

Bezdek J (1981) Pattern recognition with fuzzy objective function algorithm. Plenum Press, New YorkCrossRef

Zhang Y, He J-H, Wang S-Q, Wang P (2016) A dye removal model with a fuzzy initial condition. Therm Sci 20(3):867–870CrossRef

Wu Y, He J-H (2018) A remark on Samuelson’s variational principle in economics. Appl Math Lett 84:143–147MathSciNetCrossRef

Alawneh A, Al-Khaled K (2008) Numerical treatment of stochastic models used in statistical systems and financial markets. Comput Math Appl 56(10):2724–2732MathSciNetCrossRef

10.

Ghaneai H, Hosseini M (2016) Solving differential-algebraic equations through variational iteration method with an auxiliary parameter. Appl Math Model 40(5–6):3991–4001MathSciNetCrossRef

11.

Fritz H, Garcia-Escudero L (2013) Robust constrained fuzzy clustering. Elsevier Trans Inf Sci 245:38–52MathSciNetMATH

12.

Sabzekar M, Naghibzadeh M (2013) Fuzzy c-means improvement using relaxed constraints support vector machines. Elsevier Trans Appl Soft Comput 13(2):881–890CrossRef

13.

Zarinbal M, Zarandi MF, Turksen I (2014) Relative entropy fuzzy c-means clustering. Elsevier Trans Inf Sci 260:74–97MathSciNetMATH

14.

Kalhori MRN, Zarandi MF (2015) Interval type-2 credibilistic clustering for pattern recognition. Elsevier Trans Pattern Recognit 48(11):3652–3672CrossRef

15.

Golsefid SMM, Zarandi MF (2016) Multi-central general type-2 fuzzy clustering approach for pattern recognitions. Elsevier Trans Inf Sci 328:172–188MATH

16.

Khanali H, Vaziri B (2016) A survey on clustering algorithms for partitioning method. Int J Comput Appl 155(4):20–25

17.

Tavana M, Mavi R, Santos-Arteaga F, Dous E (2016) An extended VIKOR method using stochastic data and subjective judgments. Comput Ind Eng 97:1–29CrossRef

18.

Macedo-Cruz A, Villegas-Romero I (2012) Unsupervised classification of aerial images based on the Otsu’s method. INTECH Open Access Publisher, LondonCrossRef

19.

Sripada S (2011) Comparison of purity and entropy of k-means clustering and fuzzy c means clustering. Indian J Comput Sci Eng (IJCSE) 2(3):343–346

20.

Brun M, Sima C (2007) Model-based evaluation of clustering validation measures. Pattern Recognit 40(3):807–824CrossRef

21.

Halkidi M, Batistakis Y (2001) On clustering validation techniques. J Intell Inf Syst 17(2–3):107–145CrossRef

22.

Zaki M, Meira W Jr (2014) Data mining and analysis: fundamental concepts and algorithms. Cambridge University Press, New YorkCrossRef

23.

Liu Y, Li Z, Xiong H (2013) Understanding and enhancement of internal clustering validation measures. IEEE Trans Cybern 43(3):982–994CrossRef

24.

Desgraupes B (2013) Clustering indices, vol 1. University of Paris Ouest-Lab Modal’X, pp 1–34

25.

Asuncion A, Newman D (2015) [Online]. Available: http://www.ics.uci.edu/~mlearn/MLRepository.html. Accessed 28 March 2015

26.

Rendon E, Abundez I, Arizmendi A (2011) Internal versus external cluster validation indexes. Int J Comput Commun 5(1):27–34

Titel: An improved approach to fuzzy clustering based on FCM algorithm and extended VIKOR method
verfasst von: Hoda Khanali
Babak Vaziri
Publikationsdatum: 25.01.2019
Verlag: Springer London
Erschienen in: Neural Computing and Applications / Ausgabe 2/2020
Print ISSN: 0941-0643
Elektronische ISSN: 1433-3058
DOI: https://doi.org/10.1007/s00521-019-04035-w

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