IDEAS home Printed from https://ideas.repec.org/a/wly/jnlmpe/v2023y2023i1n5268340.html

Multicriteria Ordered the Profile Clustering Algorithm Based on PROMETHEE and Fuzzy c‐Means

Author

Listed:
  • Muhammad Adnan Bashir
  • G. Muhiuddin
  • Tabasam Rashid
  • Muhammad Shoaib Sardar

Abstract

The purpose of multicriteria clustering is to locate groups of alternatives that have comparable qualities and have been examined across multiple criteria. An ordered profile clustering is a well‐known problem, and the fuzzy c‐means clustering (FCM) technique is one of the most broadly used in every field of life. At present, FCM is for the partitioning of information into numerous clusters which are still lacking priority relations. To address the problem of finding ranking in clusters based on multicriteria in the fuzzy environment, we propose a multicriteria ordered clustering algorithm based on the partial net outranking flow of the preference organization for enrichment evaluations method (PROMETHEE) and fuzzy c‐means. Lastly, we apply the proposed algorithm to solve a real‐world targeted clustering problem regarding the human development indexes. To test the efficacy of the proposed algorithm, a comparative analysis of ordered K‐means clustering (OKM) and FCM is carried out with it.

Suggested Citation

  • Muhammad Adnan Bashir & G. Muhiuddin & Tabasam Rashid & Muhammad Shoaib Sardar, 2023. "Multicriteria Ordered the Profile Clustering Algorithm Based on PROMETHEE and Fuzzy c‐Means," Mathematical Problems in Engineering, John Wiley & Sons, vol. 2023(1).
  • Handle: RePEc:wly:jnlmpe:v:2023:y:2023:i:1:n:5268340
    DOI: 10.1155/2023/5268340
    as

    Download full text from publisher

    File URL: https://doi.org/10.1155/2023/5268340
    Download Restriction: no

    File URL: https://libkey.io/10.1155/2023/5268340?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    References listed on IDEAS

    as
    1. Constantin Zopounidis & Michael Doumpos, 1999. "Business failure prediction using the UTADIS multicriteria analysis method," Journal of the Operational Research Society, Palgrave Macmillan;The OR Society, vol. 50(11), pages 1138-1148, November.
    2. Boujelben, Mohamed Ayman, 2017. "A unicriterion analysis based on the PROMETHEE principles for multicriteria ordered clustering," Omega, Elsevier, vol. 69(C), pages 126-140.
    3. Dimitras, A. I. & Slowinski, R. & Susmaga, R. & Zopounidis, C., 1999. "Business failure prediction using rough sets," European Journal of Operational Research, Elsevier, vol. 114(2), pages 263-280, April.
    4. Jean-Claude Cosset & Jean Roy, 1991. "The Determinants of Country Risk Ratings," Journal of International Business Studies, Palgrave Macmillan;Academy of International Business, vol. 22(1), pages 135-142, March.
    5. Ishizaka, Alessio & Nemery, Philippe, 2014. "Assigning machines to incomparable maintenance strategies with ELECTRE-SORT," Omega, Elsevier, vol. 47(C), pages 45-59.
    6. De Smet, Yves & Nemery, Philippe & Selvaraj, Ramkumar, 2012. "An exact algorithm for the multicriteria ordered clustering problem," Omega, Elsevier, vol. 40(6), pages 861-869.
    7. Jeryl L. Mumpower & Steven Livingston & Thomas J. Lee, 1987. "Expert judgments of political riskiness," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 6(1), pages 51-65.
    Full references (including those not matched with items on IDEAS)

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Boujelben, Mohamed Ayman, 2017. "A unicriterion analysis based on the PROMETHEE principles for multicriteria ordered clustering," Omega, Elsevier, vol. 69(C), pages 126-140.
    2. Díaz, Raymundo & Fernández, Eduardo & Figueira, José-Rui & Navarro, Jorge & Solares, Efrain, 2023. "A new hierarchical multiple criteria ordered clustering approach as a complementary tool for sorting and ranking problems," Omega, Elsevier, vol. 117(C).
    3. Wang, Liang & Zhang, Zi-Xin & Ishizaka, Alessio & Wang, Ying-Ming & Martínez, Luis, 2023. "TODIMSort: A TODIM based method for sorting problems," Omega, Elsevier, vol. 115(C).
    4. Zhou, Fanyin & Fu, Lijun & Li, Zhiyong & Xu, Jiawei, 2022. "The recurrence of financial distress: A survival analysis," International Journal of Forecasting, Elsevier, vol. 38(3), pages 1100-1115.
    5. Shrutika Mishra & A. R. Tripathi, 2021. "AI business model: an integrative business approach," Journal of Innovation and Entrepreneurship, Springer, vol. 10(1), pages 1-21, December.
    6. Haoming Wang & Xiangdong Liu, 2021. "Undersampling bankruptcy prediction: Taiwan bankruptcy data," PLOS ONE, Public Library of Science, vol. 16(7), pages 1-17, July.
    7. Jan Schoenfelder & Mansour Zarrin & Remo Griesbaum & Ansgar Berlis, 2022. "Stroke care networks and the impact on quality of care," Health Care Management Science, Springer, vol. 25(1), pages 24-41, March.
    8. Salwa Kessioui & Michalis Doumpos & Constantin Zopounidis, 2023. "A Bibliometric Overview of the State-of-the-Art in Bankruptcy Prediction Methods and Applications," World Scientific Book Chapters, in: Emilios Galariotis & Alexandros Garefalakis & Christos Lemonakis & Marios Menexiadis & Constantin Zo (ed.), Governance and Financial Performance Current Trends and Perspectives, chapter 6, pages 123-153, World Scientific Publishing Co. Pte. Ltd..
    9. Angeliki Papana & Anastasia Spyridou, 2020. "Bankruptcy Prediction: The Case of the Greek Market," Forecasting, MDPI, vol. 2(4), pages 1-21, December.
    10. Apostolos G. Christopoulos & Ioannis G. Dokas & Iraklis Kollias & John Leventides, 2019. "An implementation of Soft Set Theory in the Variables Selection Process for Corporate Failure Prediction Models. Evidence from NASDAQ Listed Firms," Bulletin of Applied Economics, Risk Market Journals, vol. 6(1), pages 1-20.
    11. Moro, Russ & Härdle, Wolfgang Karl & Aliakbari, Saeideh & Hoffmann, Linda, 2011. "Forecasting corporate distress in the Asian and Pacific region," SFB 649 Discussion Papers 2011-023, Humboldt University Berlin, Collaborative Research Center 649: Economic Risk.
    12. Thomas E. Mckee, 2000. "Developing a bankruptcy prediction model via rough sets theory," Intelligent Systems in Accounting, Finance and Management, John Wiley & Sons, Ltd., vol. 9(3), pages 159-173, September.
    13. Chung-Ho Su, 2017. "A Novel Hybrid Learning Achievement Prediction Model: A Case Study in Gamification Education Applications (APPs)," International Journal of Information Technology & Decision Making (IJITDM), World Scientific Publishing Co. Pte. Ltd., vol. 16(02), pages 515-543, March.
    14. I. L. Beilin & V. V. Khomenko & N. V. Kalenskaya, 2019. "The Stability of the Regional Economic System Based on the Innovative Development of the Petrochemical Cluster," Academic Journal of Interdisciplinary Studies, Richtmann Publishing Ltd, vol. 8, December.
    15. Jie Sun, 2012. "Integration Of Random Sample Selection, Support Vector Machines And Ensembles For Financial Risk Forecasting With An Empirical Analysis On The Necessity Of Feature Selection," Intelligent Systems in Accounting, Finance and Management, John Wiley & Sons, Ltd., vol. 19(4), pages 229-246, October.
    16. Sheikh Rabiul Islam & William Eberle & Sheikh K. Ghafoor & Sid C. Bundy & Douglas A. Talbert & Ambareen Siraj, 2019. "Investigating bankruptcy prediction models in the presence of extreme class imbalance and multiple stages of economy," Papers 1911.09858, arXiv.org.
    17. Fernando Zambrano Farias & María del Carmen Valls Martínez & Pedro Antonio Martín-Cervantes, 2021. "Explanatory Factors of Business Failure: Literature Review and Global Trends," Sustainability, MDPI, vol. 13(18), pages 1-26, September.
    18. Nikolaos Daskalakis & Nikolaos Aggelakis & John Filos, 2022. "Applying, Updating and Comparing Bankruptcy Forecasting Models. The Case of Greece," Accounting and Management Information Systems, Faculty of Accounting and Management Information Systems, The Bucharest University of Economic Studies, vol. 21(3), pages 335-354, September.
    19. Michał Thor & Łukasz Postek, 2024. "Gated recurrent unit network: A promising approach to corporate default prediction," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 43(5), pages 1131-1152, August.
    20. Jacquet-Lagreze, Eric & Siskos, Yannis, 2001. "Preference disaggregation: 20 years of MCDA experience," European Journal of Operational Research, Elsevier, vol. 130(2), pages 233-245, April.

    More about this item

    Statistics

    Access and download statistics

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:wly:jnlmpe:v:2023:y:2023:i:1:n:5268340. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Wiley Content Delivery (email available below). General contact details of provider: https://onlinelibrary.wiley.com/journal/2629 .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.