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Feature Weighted Cutset-type Possibilistic Fuzzy C-Means Clustering Algorithm

Published: 16 May 2023 Publication History

Abstract

Possibilistic fuzzy c-means clustering (PFCM) algorithm improves the noise sensitivity of the fuzzy c-means clustering (FCM) algorithm and the serious coincident clustering phenomenon of the possibilistic c-means clustering (PCM) algorithm. However, the Euclidean distance of the PFCM treats all features equally in the clustering process and doesn't consider the imbalance between sample features, resulting in low clustering accuracy. Furthermore, as the number of clusters increases, the PFCM still has a partially coincident clustering problem due to the lack of between-class relationships of possibilistic memberships. Therefore, a feature weighted cutset-type possibilistic fuzzy c-means clustering (FW-C-PFCM) algorithm is proposed by introducing the cutset theory and feature weights into the PFCM in this paper. Firstly, the proposed FW-C-PFCM introduces a feature weighted parameter in the objective function, and different features should take different weight values according to the distribution of samples. Secondly, the cutset theory is utilized to divide the data into inner core and outer core regions, and some possibilistic memberships inside each cluster core are modified in the iterative process. Finally, the experiments on synthetic multi-class datasets and the Iris dataset show that the FW-C-PFCM improves the partially coincident clustering phenomenon and overcomes the imbalance between sample features. The comparative experiments on a color image also indicate that the FW-C-PFCM enhances the segmentation accuracy and generates better clustering results than PFCM.

References

[1]
L. A. Zadeh. 1965. Fuzzy sets. Information and Control, 8, 3, 338-353. http://dx.doi.org/10.1016/S0019-9958(65)90241-X
[2]
James C. Bezdek. 1981. Pattern recognition with fuzzy objective function algorithms. New York: Academic.
[3]
James C. Bezdek, Robert Ehrlich, and William Full. 1984. FCM: The fuzzy c-means clustering algorithm. Computers & Geosciences. 10, 2-3, 191-203. https://doi.org/10.1016/0098-3004(84)90020-7
[4]
Yuxue Chen, Shuisheng Zhou, Ximin Zhang, Dong Li, and Cui Fu. 2022. Improved fuzzy c-means clustering by varying the fuzziness parameter. Pattern Recognition Letters. 157 (May 2022), 60-66. https://doi.org/10.1016/j.patrec.2022.03.017
[5]
Rong Lan, Jiulun Fan. 2009. A Fuzzy C-means Type Clustering Algorithm on Triangular Fuzzy Numbers. 2009 Sixth International Conference on Fuzzy Systems and Knowledge Discovery. 12-16.
[6]
Raghu Krishnapuram, James M. Keller. 1993. A possibilistic approach to clustering. IEEE Trans. Fuzzy Syst. 1, 2 (May 1993), 98-110. https://doi.org/10.1109/91.227387
[7]
Nikhil R. Pal, Kuhu Pal, James M. Keller, and James C. Bezdek. 2005. A possibilistic fuzzy c-means clustering algorithm. IEEE Trans. Fuzzy Syst. 13, 4 (August 2005), 517-530. https://doi.org/10.1109/TFUZZ.2004.840099
[8]
E. Rubio, O. Castillo, and P. Melin. 2015. A new Interval Type-2 Fuzzy Possibilistic C-Means clustering algorithm. 2015 Annual Conference of the North American Fuzzy Information Processing Society (NAFIPS) held jointly with 2015 5th World Conference on Soft Computing (WConSC). 1-5. https://doi.org/10.1109/NAFIPS-WConSC.2015.7284205
[9]
László Szilágyi. 2011. Fuzzy-Possibilistic product partition: A novel robust approach to c-means clustering. International Conference Modeling Decision Artificial Intelligence (MDAI), Changsha, China. 150-161. https://doi.org/10.1007/978-3-642-22589-5_15
[10]
S. Askari, N. Montazerin, and M.H. Fazel Zarandi. 2017. Generalized Possibilistic Fuzzy C-Means with novel cluster validity indices for clustering noisy data. Applied Soft Computing. 53 (April 2017), 262-283. https://doi.org/10.1016/j.asoc.2016.12.049
[11]
Jeonghwan Gwak, Moongu Jeon. 2014. An improved kernel-induced possibilistic fuzzy c-means clustering algorithm based on dispersion control[C]. The 2014 International Conference on Control. Automation and Information Sciences (ICCAIS 2014). Gwangju, Korea. 170-175. https://doi.org/10.1109/ICCAIS.2014.7020552
[12]
Dinh-Sinh Mai, Long Thanh Ngo, and Le-Hung Trinh. 2018. Advanced Semi-Supervised Possibilistic Fuzzy C-means Clustering Using Spatial-Spectral Distance for Land-Cover Classification[C]. 2018 IEEE International Conference on Systems, Man, and Cybernetics (SMC). 4375-4380. https://doi.org/10.1109/SMC.2018.00739
[13]
B. Simhachalam, G. Ganesan. 2014. Possibilistic Fuzzy C-means Clustering on medical diagnostic Systems[C]. International Conference on Contemporary Computing and Informatics (IC3I). 1125-1129. https://doi.org/10.1109/IC3I.2014.7019729
[14]
V. Kalist, P. Ganesan, B.S. Sathish, J. Merlin Mary Jenitha, and Khamar Basha.shaik. 2015. Possiblistic-Fuzzy C-Means Clustering Approach for the Segmentation of Satellite Images in HSL Color Space. Procedia Computer Science. 57, 49-56. https://doi.org/10.1016/j.procs.2015.07.364
[15]
Joshua Zhexue Huang, Michael K. Ng, Hongqiang Rong, and Zichen Li. 2005. Automated variable weighting in k-means type clustering. IEEE Trans. Pattern Anal. Mach. Intell. 27, 5 (May 2005), 657–668. https://doi.org/10.1109/TPAMI.2005.95
[16]
Liping Jing, Michael K. Ng, Joshua Zhexue Huang. 2007. An entropy weighting k-means algorithm for subspace clustering of high-dimensional sparse data, IEEE Trans. Knowl. Data Eng. 19, 8 (August 2007), 1026–1041. https://doi.org/10.1109/TKDE.2007.1048
[17]
Hichem Frigui, Olfa Nasraoui. 2004. Unsupervised learning of prototypes and attribute weights. Pattern Recognition. 37, 3 (March 2004), 567-581. https://doi.org/10.1016/j.patcog.2003.08.002
[18]
Miin-Shen Yang, Josephine B. M. Benjamin. 2021. Feature-Weighted Possibilistic C-Means Clustering with a Feature-Reduction Framework. IEEE Transactions on Fuzzy Systems, 29, 5, 1093-1106. https://doi.org/10.1109/TFUZZ.2020.2968879
[19]
Haiyan Yu, Jiulun Fan. 2018. Cutset-type possibilistic c-means clustering algorithm. Applied Soft Computing. 64 (March 2018), 401-422. https://doi.org/10.1016/j.asoc.2017.12.024
[20]
C. L. Blake and C.J. Merz. 1998. UCI repository of machine learning databases, a huge collection of artificial and real-world data sets. http://archive.ics.uci.edu/ml/index.php
[21]
http://www.eecs.berkeley.edu/Research/Projects/CS/vision/grouping/se gbench/
[22]
Farid Garcia-Lamont, Jair Cervantes, Asdrúbal López and Lisbeth Rodriguez. 2018. Segmentation of images by color features: A survey. Neurocomputing. 292: 1-27.
[23]
Kamaldeep Joshi, Rajkumar Yadav and Sachin Allwadhi. 2016. PSNR and MSE based investigation of LSB. 2016 International Conference on Computational Techniques in Information and Communication Technologies (ICCTICT). New Delhi, India. 280-285. https://doi.org/10.1109/ICCTICT.2016.7514593
[24]
Worawit Padungsriborworn, Natee Thong-un and Weerachon Treenuson. A Study on Automatic Flaw Detection using MSSIM in Ultrasound Imaging of Steel Plate. 2019 First International Symposium on Instrumentation, Control, Artificial Intelligence, and Robotics (ICA-SYMP). 167-170. https://doi.org/10.1109/ICA-SYMP.2019.8646291
[25]
Niloofar Borzooie, Habibollah Danyali, and Mohammad Sadegh Helfroush. 2018. Modified Density-Based Data Clustering for Interactive Liver Segmentation. Journal of Image and Graphics, 6, 1 (June 2018), 84-87.
[26]
Shruti Kohli and Shashi Mehrotra. 2016. A Clustering Approach for Optimization of Search Result. Journal of Image and Graphics, 4, 1 (June 2016), 63-66.
[27]
K. Muthukaruppan, S. Thirugnanam, R. Nagarajan, M. Rizon, S. Yaacob, M. Muthukumaran, and T. Ramachandran. A Comparison of South East Asian Face Emotion Classification Based on Optimized Ellipse Data Using Clustering Technique. Journal of Image and Graphics, 3, 1 (June 2015), 1-5.

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  1. Feature Weighted Cutset-type Possibilistic Fuzzy C-Means Clustering Algorithm

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    AIPR '22: Proceedings of the 2022 5th International Conference on Artificial Intelligence and Pattern Recognition
    September 2022
    1221 pages
    ISBN:9781450396899
    DOI:10.1145/3573942
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    Published: 16 May 2023

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    Author Tags

    1. Clustering
    2. Cutset threshold
    3. Feature weights
    4. Image segmentation
    5. Possibilistic fuzzy c-means clustering

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    • the National Natural Science Foundation of China
    • Natural Science Basic Research Plan in Shaanxi Province of China
    • the Shaanxi and in part by the New Star Team of Xi?an University of Posts & Telecommunications

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