IDEAS home Printed from https://ideas.repec.org/a/spr/compst/v37y2022i4d10.1007_s00180-021-01187-z.html
   My bibliography  Save this article

A new non-archimedean metric on persistent homology

Author

Listed:
  • İsmail Güzel

    (Istanbul Technical University)

  • Atabey Kaygun

    (Istanbul Technical University)

Abstract

In this article, we define a new non-archimedean metric structure, called cophenetic metric, on persistent homology classes of all degrees. We then show that zeroth persistent homology together with the cophenetic metric and hierarchical clustering algorithms with a number of different metrics do deliver statistically verifiable commensurate topological information based on experimental results we obtained on different datasets. We also observe that the resulting clusters coming from cophenetic distance do shine in terms of different evaluation measures such as silhouette score and the Rand index. Moreover, since the cophenetic metric is defined for all homology degrees, one can now display the inter-relations of persistent homology classes in all degrees via rooted trees.

Suggested Citation

  • İsmail Güzel & Atabey Kaygun, 2022. "A new non-archimedean metric on persistent homology," Computational Statistics, Springer, vol. 37(4), pages 1963-1983, September.
  • Handle: RePEc:spr:compst:v:37:y:2022:i:4:d:10.1007_s00180-021-01187-z
    DOI: 10.1007/s00180-021-01187-z
    as

    Download full text from publisher

    File URL: http://link.springer.com/10.1007/s00180-021-01187-z
    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/s00180-021-01187-z?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. Alberto Lumbreras & Julien Velcin & Marie Guégan & Bertrand Jouve, 2017. "Non-parametric clustering over user features and latent behavioral functions with dual-view mixture models," Computational Statistics, Springer, vol. 32(1), pages 145-177, March.
    2. J. Hartigan, 1985. "Statistical theory in clustering," Journal of Classification, Springer;The Classification Society, vol. 2(1), pages 63-76, December.
    3. Lawrence Hubert & Phipps Arabie, 1985. "Comparing partitions," Journal of Classification, Springer;The Classification Society, vol. 2(1), pages 193-218, December.
    4. Stephen Johnson, 1967. "Hierarchical clustering schemes," Psychometrika, Springer;The Psychometric Society, vol. 32(3), pages 241-254, September.
    5. Volodymyr Melnykov & Xuwen Zhu, 2019. "An extension of the K-means algorithm to clustering skewed data," Computational Statistics, Springer, vol. 34(1), pages 373-394, March.
    6. H. W. Kuhn, 2005. "The Hungarian method for the assignment problem," Naval Research Logistics (NRL), John Wiley & Sons, vol. 52(1), pages 7-21, February.
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. Ezgi Gülenç Bayirli & Atabey Kaygun & Ersoy Öz, 2023. "An Analysis of PISA 2018 Mathematics Assessment for Asia-Pacific Countries Using Educational Data Mining," Mathematics, MDPI, vol. 11(6), pages 1-23, March.

    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. Satoru Yokoyama & Atsuho Nakayama & Akinori Okada, 2009. "One-mode three-way overlapping cluster analysis," Computational Statistics, Springer, vol. 24(1), pages 165-179, February.
    2. Remizov, Alexey & Memon, Shazim Ali & Kim, Jong R., 2024. "Novel building energy performance-based climate zoning enhanced with spatial constraint," Applied Energy, Elsevier, vol. 355(C).
    3. Bocci, Laura & Vicari, Donatella & Vichi, Maurizio, 2006. "A mixture model for the classification of three-way proximity data," Computational Statistics & Data Analysis, Elsevier, vol. 50(7), pages 1625-1654, April.
    4. Simon Blanchard & Daniel Aloise & Wayne DeSarbo, 2012. "The Heterogeneous P-Median Problem for Categorization Based Clustering," Psychometrika, Springer;The Psychometric Society, vol. 77(4), pages 741-762, October.
    5. Weinand, J.M. & McKenna, R. & Fichtner, W., 2019. "Developing a municipality typology for modelling decentralised energy systems," Utilities Policy, Elsevier, vol. 57(C), pages 75-96.
    6. Sun Jiehuan & Warren Joshua L. & Zhao Hongyu, 2017. "A Bayesian semiparametric factor analysis model for subtype identification," Statistical Applications in Genetics and Molecular Biology, De Gruyter, vol. 16(2), pages 145-158, April.
    7. Ahmed Albatineh & Magdalena Niewiadomska-Bugaj, 2011. "Correcting Jaccard and other similarity indices for chance agreement in cluster analysis," 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. 5(3), pages 179-200, October.
    8. Matthijs J. Warrens & Hanneke Hoef, 2022. "Understanding the Adjusted Rand Index and Other Partition Comparison Indices Based on Counting Object Pairs," Journal of Classification, Springer;The Classification Society, vol. 39(3), pages 487-509, November.
    9. Jeffrey L. Andrews & Ryan Browne & Chelsey D. Hvingelby, 2022. "On Assessments of Agreement Between Fuzzy Partitions," Journal of Classification, Springer;The Classification Society, vol. 39(2), pages 326-342, July.
    10. Kemmawadee Preedalikit & Daniel Fernández & Ivy Liu & Louise McMillan & Marta Nai Ruscone & Roy Costilla, 2024. "Row mixture-based clustering with covariates for ordinal responses," Computational Statistics, Springer, vol. 39(5), pages 2511-2555, July.
    11. Klutchnikoff, Nicolas & Poterie, Audrey & Rouvière, Laurent, 2022. "Statistical analysis of a hierarchical clustering algorithm with outliers," Journal of Multivariate Analysis, Elsevier, vol. 192(C).
    12. Linda Vidman & David Källberg & Patrik Rydén, 2019. "Cluster analysis on high dimensional RNA-seq data with applications to cancer research - An evaluation study," PLOS ONE, Public Library of Science, vol. 14(12), pages 1-21, December.
    13. Andrea Di Iura, 2022. "Comparison of empirical and shrinkage correlation algorithm for clustering methods in the futures market," SN Business & Economics, Springer, vol. 2(8), pages 1-17, August.
    14. Ranjan Maitra & Ivan P. Ramler, 2009. "Clustering in the Presence of Scatter," Biometrics, The International Biometric Society, vol. 65(2), pages 341-352, June.
    15. Chia-Yi Chiu & Hans-Friedrich Köhn, 2016. "Consistency of Cluster Analysis for Cognitive Diagnosis: The Reduced Reparameterized Unified Model and the General Diagnostic Model," Psychometrika, Springer;The Psychometric Society, vol. 81(3), pages 585-610, September.
    16. Miriam Aparicio, 2021. "Resiliency and Cooperation or Regarding Social and Collective Competencies for University Achievement. An Analysis from a Systemic Perspective," European Journal of Social Sciences Education and Research Articles, Revistia Research and Publishing, vol. 8, ejser_v8_.
    17. Yunpeng Zhao & Qing Pan & Chengan Du, 2019. "Logistic regression augmented community detection for network data with application in identifying autism‐related gene pathways," Biometrics, The International Biometric Society, vol. 75(1), pages 222-234, March.
    18. Wu, Han-Ming & Tien, Yin-Jing & Chen, Chun-houh, 2010. "GAP: A graphical environment for matrix visualization and cluster analysis," Computational Statistics & Data Analysis, Elsevier, vol. 54(3), pages 767-778, March.
    19. Claudia Quinteros-Cartaya & Guillermo Solorio-Magaña & Francisco Javier Núñez-Cornú & Felipe de Jesús Escalona-Alcázar & Diana Núñez, 2023. "Microearthquakes in the Guadalajara Metropolitan Zone, Mexico: evidence from buried active faults in Tesistán Valley, Zapopan," Natural Hazards: Journal of the International Society for the Prevention and Mitigation of Natural Hazards, Springer;International Society for the Prevention and Mitigation of Natural Hazards, vol. 116(3), pages 2797-2818, April.
    20. José E. Chacón, 2021. "Explicit Agreement Extremes for a 2 × 2 Table with Given Marginals," Journal of Classification, Springer;The Classification Society, vol. 38(2), pages 257-263, July.

    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:compst:v:37:y:2022:i:4:d:10.1007_s00180-021-01187-z. 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.