IDEAS home Printed from https://ideas.repec.org/a/spr/jclass/v3y1986i1p55-60.html
   My bibliography  Save this article

Computing the nearest neighbor interchange metric for unlabeled binary trees is NP-complete

Author

Listed:
  • Mirko Křivánek

Abstract

No abstract is available for this item.

Suggested Citation

  • Mirko Křivánek, 1986. "Computing the nearest neighbor interchange metric for unlabeled binary trees is NP-complete," Journal of Classification, Springer;The Classification Society, vol. 3(1), pages 55-60, March.
    • Handle: RePEc:spr:jclass:v:3:y:1986:i:1:p:55-60
    DOI: 10.1007/BF01896811
    as

    Download full text from publisher

    File URL: http://hdl.handle.net/10.1007/BF01896811
    Download Restriction: Access to full text is restricted to subscribers.
    File URL: https://libkey.io/10.1007/BF01896811?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. William Day & Herbert Edelsbrunner, 1984. "Efficient algorithms for agglomerative hierarchical clustering methods," Journal of Classification, Springer;The Classification Society, vol. 1(1), pages 7-24, December.
    2. Edward Brown & William Day, 1984. "A computationally efficient approximation to the nearest neighbor interchange metric," Journal of Classification, Springer;The Classification Society, vol. 1(1), pages 93-124, December.
    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. William Day & Herbert Edelsbrunner, 1985. "Investigation of proportional link linkage clustering methods," Journal of Classification, Springer;The Classification Society, vol. 2(1), pages 239-254, December.
    2. Monika Khandelwal & Sabha Sheikh & Ranjeet Kumar Rout & Saiyed Umer & Saurav Mallik & Zhongming Zhao, 2022. "Unsupervised Learning for Feature Representation Using Spatial Distribution of Amino Acids in Aldehyde Dehydrogenase (ALDH2) Protein Sequences," Mathematics, MDPI, vol. 10(13), pages 1-20, June.
    3. Alberto Fernández & Sergio Gómez, 2020. "Versatile Linkage: a Family of Space-Conserving Strategies for Agglomerative Hierarchical Clustering," Journal of Classification, Springer;The Classification Society, vol. 37(3), pages 584-597, October.
    4. C. Finden & A. Gordon, 1985. "Obtaining common pruned trees," Journal of Classification, Springer;The Classification Society, vol. 2(1), pages 255-276, December.
    5. Taneja, Anu & Arora, Anuja, 2019. "Modeling user preferences using neural networks and tensor factorization model," International Journal of Information Management, Elsevier, vol. 45(C), pages 132-148.
    6. Yuching Lu & Koki Tozuka & Goutam Chakraborty & Masafumi Matsuhara, 2021. "A Novel Item Cluster-Based Collaborative Filtering Recommendation System," The Review of Socionetwork Strategies, Springer, vol. 15(2), pages 327-346, November.
    7. Sandra Mayr & Fabian Hauser & Sujitha Puthukodan & Markus Axmann & Janett Göhring & Jaroslaw Jacak, 2020. "Statistical analysis of 3D localisation microscopy images for quantification of membrane protein distributions in a platelet clot model," PLOS Computational Biology, Public Library of Science, vol. 16(6), pages 1-34, June.
    8. Cheng-Chun Lee & Mikel Maron & Ali Mostafavi, 2022. "Community-scale big data reveals disparate impacts of the Texas winter storm of 2021 and its managed power outage," Palgrave Communications, Palgrave Macmillan, vol. 9(1), pages 1-12, December.
    9. Li, Daolun & Zhou, Xia & Xu, Yanmei & Wan, Yujin & Zha, Wenshu, 2023. "Deep learning-based analysis of the main controlling factors of different gas-fields recovery rate," Energy, Elsevier, vol. 285(C).
    10. Bajoulvand, Atena & Zargari Marandi, Ramtin & Daliri, Mohammad Reza & Sabzpoushan, Seyed Hojjat, 2017. "Analysis of folk music preference of people from different ethnic groups using kernel-based methods on EEG signals," Applied Mathematics and Computation, Elsevier, vol. 307(C), pages 62-70.
    11. Ji, Yuxuan & Geroliminis, Nikolas, 2012. "On the spatial partitioning of urban transportation networks," Transportation Research Part B: Methodological, Elsevier, vol. 46(10), pages 1639-1656.
    12. Tien-Chin Wang & Binh Ngoc Phan & Thuy Thi Thu Nguyen, 2021. "Evaluating Operation Performance in Higher Education: The Case of Vietnam Public Universities," Sustainability, MDPI, vol. 13(7), pages 1-21, April.
    13. Bock, Hans H., 1996. "Probabilistic models in cluster analysis," Computational Statistics & Data Analysis, Elsevier, vol. 23(1), pages 5-28, November.
    14. James Ming Chen & Mobeen Ur Rehman, 2021. "A Pattern New in Every Moment: The Temporal Clustering of Markets for Crude Oil, Refined Fuels, and Other Commodities," Energies, MDPI, vol. 14(19), pages 1-58, September.
    15. Cho, Catherine & Kim, Sooyoung & Lee, Jaewook & Lee, Dae-Won, 2006. "A tandem clustering process for multimodal datasets," European Journal of Operational Research, Elsevier, vol. 168(3), pages 998-1008, February.
    16. Chen, James Ming & Rehman, Mobeen Ur & Vo, Xuan Vinh, 2021. "Clustering commodity markets in space and time: Clarifying returns, volatility, and trading regimes through unsupervised machine learning," Resources Policy, Elsevier, vol. 73(C).
    17. Costas Panagiotakis, 2015. "Point Clustering via Voting Maximization," Journal of Classification, Springer;The Classification Society, vol. 32(2), pages 212-240, July.
    18. Claudiu Vinte & Marcel Ausloos, 2022. "The Cross-Sectional Intrinsic Entropy. A Comprehensive Stock Market Volatility Estimator," Papers 2205.00104, arXiv.org.
    19. Lerato Lerato & Thomas Niesler, 2015. "Clustering Acoustic Segments Using Multi-Stage Agglomerative Hierarchical Clustering," PLOS ONE, Public Library of Science, vol. 10(10), pages 1-24, October.
    20. Quan Gan & Wang Chun Wei & David Johnstone, 2015. "A faster estimation method for the probability of informed trading using hierarchical agglomerative clustering," Quantitative Finance, Taylor & Francis Journals, vol. 15(11), pages 1805-1821, November.

    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:jclass:v:3:y:1986:i:1:p:55-60. 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.