IDEAS home Printed from https://ideas.repec.org/a/spr/annopr/v303y2021i1d10.1007_s10479-018-3043-4.html
   My bibliography  Save this article

Validity indices for clusters of uncertain data objects

Author

Listed:
  • Behnam Tavakkol

    (Stockton University)

  • Myong K. Jeong

    (Rutgers University)

  • Susan L. Albin

    (Rutgers University)

Abstract

Clustering validity indices are the main tools for evaluating the quality of formed clusters and determining the correct number of clusters. They can be applied on the results of clustering algorithms to validate the performance of those algorithms. In this paper, two clustering validity indices named uncertain Silhouette and Order Statistic, are developed for uncertain data. To the best of our knowledge, there is not any clustering validity index in the literature that is designed for uncertain objects and can be used for validating the performance of uncertain clustering algorithms. Our proposed validity indices use probabilistic distance measures to capture the distance between uncertain objects. They outperform existing validity indices for certain data in validating clusters of uncertain data objects and are robust to outliers. The Order Statistic index in particular, a general form of uncertain Dunn validity index (also developed here), is well capable of handling instances where there is a single cluster that is relatively scattered (not compact) compared to other clusters, or there are two clusters that are close (not well-separated) compared to other clusters. The aforementioned instances can potentially result in the failure of existing clustering validity indices in detecting the correct number of clusters.

Suggested Citation

  • Behnam Tavakkol & Myong K. Jeong & Susan L. Albin, 2021. "Validity indices for clusters of uncertain data objects," Annals of Operations Research, Springer, vol. 303(1), pages 321-357, August.
    • Handle: RePEc:spr:annopr:v:303:y:2021:i:1:d:10.1007_s10479-018-3043-4
    DOI: 10.1007/s10479-018-3043-4
    as

    Download full text from publisher

    File URL: http://link.springer.com/10.1007/s10479-018-3043-4
    File Function: Abstract
    Download Restriction: Access to the full text of the articles in this series is restricted.
    File URL: https://libkey.io/10.1007/s10479-018-3043-4?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
    ---><---

    As the access to this document is restricted, you may want to search for a different version of it.

    References listed on IDEAS

    as
    1. Lian Duan & Lida Xu & Ying Liu & Jun Lee, 2009. "Cluster-based outlier detection," Annals of Operations Research, Springer, vol. 168(1), pages 151-168, April.
    2. Zengchang Qin & Tao Wan & Hanqing Zhao, 2017. "Hybrid clustering of data and vague concepts based on labels semantics," Annals of Operations Research, Springer, vol. 256(2), pages 393-416, September.
    3. Yannis Marinakis & Magdalene Marinaki & Michael Doumpos & Nikolaos Matsatsinis & Constantin Zopounidis, 2011. "A hybrid ACO-GRASP algorithm for clustering analysis," Annals of Operations Research, Springer, vol. 188(1), pages 343-358, August.
    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. Philippe Bernard & Najat El Mekkaoui De Freitas & Bertrand B. Maillet, 2022. "A financial fraud detection indicator for investors: an IDeA," Annals of Operations Research, Springer, vol. 313(2), pages 809-832, June.
    2. Marek Śmieja & Magdalena Wiercioch, 2017. "Constrained clustering with a complex cluster structure," Advances in Data Analysis and Classification, Springer;German Classification Society - Gesellschaft für Klassifikation (GfKl);Japanese Classification Society (JCS);Classification and Data Analysis Group of the Italian Statistical Society (CLADAG);International Federation of Classification Societies (IFCS), vol. 11(3), pages 493-518, September.
    3. Aielli, Gian Piero & Caporin, Massimiliano, 2013. "Fast clustering of GARCH processes via Gaussian mixture models," Mathematics and Computers in Simulation (MATCOM), Elsevier, vol. 94(C), pages 205-222.
    4. Ali Tosyali & Jinho Kim & Jeongsub Choi & Yunyi Kang & Myong K. Jeong, 2020. "New node anomaly detection algorithm based on nonnegative matrix factorization for directed citation networks," Annals of Operations Research, Springer, vol. 288(1), pages 457-474, May.
    5. Hae-Sang Park & Jeonghwa Lee & Chi-Hyuck Jun, 2014. "Clustering noise-included data by controlling decision errors," Annals of Operations Research, Springer, vol. 216(1), pages 129-144, May.
    6. Rakin Abrar & Showmitra Kumar Sarkar & Kashfia Tasnim Nishtha & Swapan Talukdar & Shahfahad & Atiqur Rahman & Abu Reza Md Towfiqul Islam & Amir Mosavi, 2022. "Assessing the Spatial Mapping of Heat Vulnerability under Urban Heat Island (UHI) Effect in the Dhaka Metropolitan Area," Sustainability, MDPI, vol. 14(9), pages 1-24, April.
    7. Farnè, Matteo & Vouldis, Angelos T., 2018. "A methodology for automised outlier detection in high-dimensional datasets: an application to euro area banks' supervisory data," Working Paper Series 2171, European Central Bank.
    8. Xin Yao & Yuanyuan Cheng & Li Zhou & Malin Song, 2022. "Green efficiency performance analysis of the logistics industry in China: based on a kind of machine learning methods," Annals of Operations Research, Springer, vol. 308(1), pages 727-752, January.
    9. Alexandra CERNIAN & Dorin CARSTOIU & Adriana OLTEANU & Valentin SGARCIU, 2016. "Assessing the Performance of Compression Based Clustering for Text Mining," ECONOMIC COMPUTATION AND ECONOMIC CYBERNETICS STUDIES AND RESEARCH, Faculty of Economic Cybernetics, Statistics and Informatics, vol. 50(2), pages 197-210.
    10. Andrea Flori & Simone Giansante & Claudia Girardone & Fabio Pammolli, 2021. "Banks’ business strategies on the edge of distress," Annals of Operations Research, Springer, vol. 299(1), pages 481-530, April.
    11. Shouhui Pan & Li Wang & Kaiyi Wang & Zhuming Bi & Siqing Shan & Bo Xu, 2014. "A Knowledge Engineering Framework for Identifying Key Impact Factors from Safety‐Related Accident Cases," Systems Research and Behavioral Science, Wiley Blackwell, vol. 31(3), pages 383-397, May.

    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:spr:annopr:v:303:y:2021:i:1:d:10.1007_s10479-018-3043-4. 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: Sonal Shukla or Springer Nature Abstracting and Indexing (email available below). General contact details of provider: http://www.springer.com .

    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.