Skip to main content
SpringerLink
Log in

Some Other Topics in Agglomerative Hierarchical Clustering

  • Chapter
  • First Online:
Theory of Agglomerative Hierarchical Clustering

Part of the book series: Behaviormetrics: Quantitative Approaches to Human Behavior ((BQAHB,volume 15))

  • 475 Accesses

Abstract

Several other topics in agglomerative hierarchical clustering studied by the author and his colleagues are described in this chapter. They are as follows.

This is a preview of subscription content, log in via an institution to check access.

Access this chapter

Log in via an institution
Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 89.00
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 119.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info
Hardcover Book
USD 119.99
Price excludes VAT (USA)
  • Durable hardcover edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

Notes

  1. 1.

    We do not call this method ‘a median method’, since it is different from ‘the median method’ proposed by Gower [19]. See Chap. 6.

References

  1. M. Ester, H.-P. Kriegel, J. Sander, X. Xu, A density-based algorithm for discovering clusters in large spatial databases with noise, in KDD-96 Proceedings (1996), pp. 226–231

    Google Scholar 

  2. D. Wishart, Mode analysis: a generalization of nearest neighbor which reduces chaining effects, in ed by A.J. Cole, Numerical Taxonomy, Proceedings of the Colloquium, in Numerical Taxonomy (University St Andrews, 1968), pp. 283–311

    Google Scholar 

  3. E. Schubert, J. Sander, M. Ester, H.-P. Kriegel, X. Xu, DBSCAN revisited: why and how you should (still) use DBSCAN. ACM Trans. Database Syst. 42(3), 1–21 (2017)

    Article  MathSciNet  Google Scholar 

  4. S. Miyahara, S. Miyamoto, A family of algorithms using spectral clustering and DBSCAN, in Proceedings of 2014 IEEE International Conference on Granular Computing (GrC2014), Noboribetsu, Hokkaido, Japan, 22–24 Oct 2014 (2014), pp. 196–200

    Google Scholar 

  5. V.N. Vapnik, Statistical Learning Theory (Wiley, Neww York, 1998)

    Google Scholar 

  6. O. Chapelle, A. Zien, B. Scholköpf (eds.), Semi-Supervised Learning (MIT Press, Cambridge, Massachusetts, USA, 2006)

    Google Scholar 

  7. X. Zhu, A.B. Goldberg, Introduction to Semi-Supervised Learning (Morgan and Claypool Publishers, San Rafael, CA, USA, 2009)

    Google Scholar 

  8. R.O. Duda, P.E. Hart, Pattern Classification and Scene Analysis (Wiley, New York, 1973)

    Google Scholar 

  9. S. Miyamoto, Introduction to Cluster Analysis (Morikita-Shuppan, Tokyo, 2000). ((in Japanese))

    Google Scholar 

  10. D. Arthur, S. Vassilvitskii, \(k\)-means++: the advantages of careful seeding, in Proceedings of the Eighteenth Annual ACM-SIAM Symposium on Discrete Algorithms, Society for Industrial and Applied Mathematics Philadelphia, PA, USA (2007), pp. 1027–1035

    Google Scholar 

  11. Y. Tamura, S. Miyamoto, A method of two stage clustering using agglomerative hierarchical algorithms with one-pass k-Means++ or k-Median++, inProceedings of 2014 IEEE International Conference on Granular Computing (GrC2014) (Noboribetsu, Hokkaido, Japan, 22–24 Oct 2014), pp. 281–285

    Google Scholar 

  12. S. Basu, I. Davidson, K. Wagstaff (eds.), Constrained Clustering: Advances in Algorithms, Theory, and Applications (Chapman & Hall/CRC, Boca Raton, FL, USA, 2009)

    Google Scholar 

  13. I. Davidson, S.S. Ravi, Agglomerative hierarchical clustering with constraints: theoretical and empirical results. Knowl. Discov. Databases: PKDD 2005(LNCS3721), 59–70 (2005)

    Google Scholar 

  14. P. Giordani, M.B. Ferraro, F. Martella, An Introduction to Clustering with R (Springer Nature, Singapore, 2020)

    Google Scholar 

  15. G.J. McLachlan, D. Peel, Finite Mixture Models (Wiley, New York, 2000)

    Google Scholar 

  16. G.J. McLachlan, T. Krishnan, The EM Algorithm and Extensions, 2nd edn. (Wiley, Hoboken, NJ, 2008)

    Google Scholar 

  17. M.R. Anderberg, Cluster Analysis for Applications (Academic Press, New York, 1973)

    Google Scholar 

  18. S. Miyamoto, Fuzzy Sets in Information Retrieval and Cluster Analysis (Springer, Heidelberg, 1990)

    Google Scholar 

  19. J.C. Gower, A comparison of some methods of cluster analysis. Biometrics 23, 623–637 (1967)

    Article  Google Scholar 

  20. A. Okada, T. Iwamoto, A comparison before and after the joint first stage achievement test by asymmetric cluster analysis. Behaviormetrika 23(2), 169–185 (1996)

    Article  Google Scholar 

  21. T. Saito, H. Yadohisa, Data Analysis of Asymmetric Structures (Marcel Dekker, New York, 2005)

    Google Scholar 

  22. S.M. Stigler, Citation patterns in the journals of statistics and probability. Stat. Sci. 9, 94–108 (1994)

    Article  Google Scholar 

  23. S. Takumi, S. Miyamoto, Top-down versus Bottom-up methods of linkage for asymmetric agglomerative hierarchical clustering, in Proceedings of 2012 IEEE International Conference on Granular Computing (11–12 Aug, Hangzhou, China, 2012), pp. 542–547

    Google Scholar 

Download references

Author information

Authors and Affiliations

  1. University of Tsukuba, Tsukuba, Ibaraki, Japan

    Sadaaki Miyamoto

Authors
  1. Sadaaki Miyamoto
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Sadaaki Miyamoto .

Rights and permissions

Reprints and permissions

Copyright information

© 2022 Springer Nature Singapore Pte Ltd.

About this chapter

Check for updates. Verify currency and authenticity via CrossMark

Cite this chapter

Miyamoto, S. (2022). Some Other Topics in Agglomerative Hierarchical Clustering. In: Theory of Agglomerative Hierarchical Clustering. Behaviormetrics: Quantitative Approaches to Human Behavior, vol 15. Springer, Singapore. https://doi.org/10.1007/978-981-19-0420-2_5

Download citation

Publish with us

Policies and ethics

Search

Navigation