skip to main content
10.1145/3641584.3641776acmotherconferencesArticle/Chapter ViewAbstractPublication PagesaiprConference Proceedingsconference-collections
research-article

An Efficient Weighted Fuzzy Possibilistic C-Means Clustering Algorithm

Published: 14 June 2024 Publication History

Abstract

The fuzzy possibilistic c-means clustering (FPCM) algorithm is a hybrid clustering algorithm by incorporating fuzzy memberships and possibilistic memberships into the objective function, which can improve the anti-noise ability of the fuzzy c-means clustering (FCM) and the coincident clustering phenomenon of the possibilistic c-means clustering (PCM) algorithm to some extent. However, the FPCM algorithm often obtains poor results for datasets injected with strong long-distance noise, resulting from that these noise points are assigned with too large values for fuzzy memberships. Therefore, this paper introduces a weight parameter into the objective function of the FPCM and proposes an efficient weighted fuzzy possibilistic c-means clustering (EWFPCM) algorithm. The experimental results on several synthetic datasets and color image segmentation show that the proposed EWFPCM algorithm can greatly improve the anti-noise ability of the FPCM, and performs best among compared several clustering algorithms.

References

[1]
Farid Garcia-Lamont, Jair Cervantes, Asdrúbal López and Lisbeth Rodriguez.2018. Segmentation of images by color features: A survey. Neurocomputing. 292:1-27
[2]
Chengmao Wu and Xue Zhang. 2022. Total Bregman divergence-driven possibilistic fuzzy clustering with kernel metric and local information for grayscale image segmentation. Pattern Recognition. 128, 1-18. https://doi.org/10.1016/j.patcog.2022.108686
[3]
Liang Wang, Xin Geng, James Bezdek, Christopher Leckie and Ramamohanarao. Kotagiri. 2010. Enhanced Visual Analysis for Cluster Tendency Assessment and Data Partitioning. IEEE Transactions on Knowledge and Data Engineering. 22(10), 1401-1414.
[4]
Ni Bin. 2018. Research on Methods and Techniques for IoT Big Data Cluster Analysis. 2018 International Conference on Information Systems and Computer Aided Education (ICISCAE). 184-188.
[5]
Zexuan Ji, Quansen Sun, and Deshen Xia. 2011. A modified possibilistic fuzzy c-means clustering algorithm for bias field estimation and segmentation of brain MR image. Computerized Medical Imaging and Graphics. 35(5), 383-397. https://doi.org/10.1016/j.compmedimag.2010.12.001
[6]
Mohammad M. Ershadi an Abbas Seifi. 2022. Applications of dynamic feature selection and clustering methods to medical diagnosis. Applied Soft Computing, 126, 1-18. https://doi.org/10.1016/j.asoc.2022.109293
[7]
L. A. Zadeh. 1965. Fuzzy sets. Information and Control, 8, 3, 338-353. http://dx.doi.org/10.1016/S0019-9958(65)90241-X
[8]
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
[9]
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
[10]
Nikhil R. Pal, Kuhu Pal, and James C. Bezdek. 1997. A Mixed c-Means Clustering Model. Proceedings of 6th International Fuzzy Systems Conference, Barcelona, Spain. 1, 11-21.
[11]
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
[12]
Hossein Saberi, Reza Sharbati and Behzad Farzanegan. 2022. A gradient ascent algorithm based on possibilistic fuzzy c-means for clustering noisy data. Expert Systems with Applications, 191, 116153, 1-20. https://doi.org/10.1016/j.eswa.2021.116153
[13]
S. Askari, N. Montazerin, M. H. Fazel Zarandi, and E. Hakimi. 2017. Generalized entropy based possibilistic fuzzy c-means for clustering noisy data and its convergence proof. Neurocomputing, 219, 186-202. http://dx.doi.org/10.1016/j.neucom.2016.09.025
[14]
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), Redmond, WA, USA, 2015. 1-5.
[15]
Krishna K. Chintalapudi and Moshe Kam. 1998. A Noise-Resistant Fuzzy C Means Algorithm for Clustering. 1998 IEEE International Conference on Fuzzy Systems Proceedings. IEEE World Congress on Computational Intelligence (Cat. No.98CH36228), Anchorage, AK, USA, 1998. 2, 1458-1463.
[16]
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
[17]
Stelios Krinidis and Vassilios Chatzis. 2010. A Robust Fuzzy Local Information C-Means Clustering Algorithm. IEEE Transactions on Image Processing, 19(5), 1328-1337.
[18]
Qingsheng Wang, Xiaopeng Wang, Chao Fang, and Wenting Yang. 2020. Robust fuzzy c-means clustering algorithm with adaptive spatial and intensity constraint and membership linking for noise image segmentation. Applied Soft Computing. 92. https://doi.org/10.1016/j.asoc.2020.106318
[19]
http://www.eecs.berkeley.edu/Research/Projects/CS/vision/grouping/segbench/
[20]
https://ivrlwww.epfl.ch/supplementary_material/RK_CVPR09/index.html
[21]
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
[22]
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/ICASYMP.2019.8646291

Recommendations

Comments

Information & Contributors

Information

Published In

cover image ACM Other conferences
AIPR '23: Proceedings of the 2023 6th International Conference on Artificial Intelligence and Pattern Recognition
September 2023
1540 pages
ISBN:9798400707674
DOI:10.1145/3641584
Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than the author(s) must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected].

Publisher

Association for Computing Machinery

New York, NY, United States

Publication History

Published: 14 June 2024

Permissions

Request permissions for this article.

Check for updates

Author Tags

  1. Clustering algorithm
  2. Fuzzy possibilistic c-means clustering
  3. Image segmentation
  4. Weight

Qualifiers

  • Research-article
  • Research
  • Refereed limited

Conference

AIPR 2023

Contributors

Other Metrics

Bibliometrics & Citations

Bibliometrics

Article Metrics

  • 0
    Total Citations
  • 14
    Total Downloads
  • Downloads (Last 12 months)14
  • Downloads (Last 6 weeks)2
Reflects downloads up to 16 Feb 2025

Other Metrics

Citations

View Options

Login options

View options

PDF

View or Download as a PDF file.

PDF

eReader

View online with eReader.

eReader

HTML Format

View this article in HTML Format.

HTML Format

Figures

Tables

Media

Share

Share

Share this Publication link

Share on social media