References

How to Cite

When using genieclust in research publications, please cite [11] and [16] as specified below. Thank you.

See Also

• fastcluster: http://www.danifold.net/fastcluster.html

• hdbscan: https://github.com/scikit-learn-contrib/hdbscan

• ITM: https://github.com/amueller/information-theoretic-mst

• mlpack: https://www.mlpack.org/

• nmslib: https://github.com/nmslib/nmslib/tree/master/python_bindings

• scikit-learn: https://scikit-learn.org/stable/modules/clustering.html

Bibliography

[1]

Buitinck, L., et al. (2013). API design for machine learning software: Experiences from the scikit-learn project. In: ECML PKDD Workshop: Languages for Data Mining and Machine Learning, pp. 108–122.

[2]

Campello, R.J.G.B., Moulavi, D., Sander, J. (2013). Density-based clustering based on hierarchical density estimates. Lecture Notes in Computer Science, 7819:160–172. DOI: 10.1007/978-3-642-37456-2_14.

[3]

Cena, A. (2018). Adaptive hierarchical clustering algorithms based on data aggregation methods. PhD thesis, Systems Research Institute, Polish Academy of Sciences.

[4]

Curtin, R.R., Edel, M., Lozhnikov, M., Mentekidis, Y., Ghaisas, S., Zhang, S. (2018). Mlpack 3: A fast, flexible machine learning library. Journal of Open Source Software, 3(26):726. DOI: 10.21105/joss.00726.

[5]

Dasgupta, S., Ng, V. (2009). Single data, multiple clusterings. In: Proc. NIPS Workshop Clustering: Science or Art? Towards Principled Approaches. URL: https://clusteringtheory.org.

[6]

Dua, D., Graff, C. (2019). UCI machine learning repository. URL: http://archive.ics.uci.edu/ml.

[7]

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

[8]

Fowlkes, E.B., Mallows, C.L. (1983). A method for comparing two hierarchical clusterings. Journal of the American Statistical Association, 78(383):553–569.

[9]

Fränti, P., Mariescu-Istodor, R., Zhong, C. (2016). XNN graph. Lecture Notes in Computer Science, 10029:207–217. DOI: 10.1007/978-3-319-49055-7_19.

[10]

Fränti, P., Sieranoja, S. (2018). K-means properties on six clustering benchmark datasets. Applied Intelligence, 48(12):4743–4759. DOI: 10.1007/s10489-018-1238-7.

[11]

Gagolewski, M. (2021). genieclust: Fast and robust hierarchical clustering. SoftwareX, 15:100722. DOI: 10.1016/j.softx.2021.100722.

[12]

Gagolewski, M. (2022). A framework for benchmarking clustering algorithms. SoftwareX, 20:101270. URL: https://clustering-benchmarks.gagolewski.com, DOI: 10.1016/j.softx.2022.101270.

[13]

Gagolewski, M. (2022). Adjusted asymmetric accuracy: A well-behaving external cluster validity measure. under review (preprint). URL: https://arxiv.org/pdf/2209.02935.pdf, DOI: 10.48550/arXiv.2209.02935.

[14]

Gagolewski, M. (2022). Minimalist Data Wrangling with Python. Zenodo, Melbourne. ISBN 978-0-6455719-1-2. URL: https://datawranglingpy.gagolewski.com/, DOI: 10.5281/zenodo.6451068.

[15]

Gagolewski, M. (2023). Deep R Programming. Zenodo, Melbourne. ISBN 978-0-6455719-2-9 (reserved). early draft. URL: https://deepr.gagolewski.com/, DOI: 10.5281/zenodo.7490464.

[16]

Gagolewski, M., Bartoszuk, M., Cena, A. (2016). Genie: A new, fast, and outlier-resistant hierarchical clustering algorithm. Information Sciences, 363:8–23. DOI: 10.1016/j.ins.2016.05.003.

[17]

Gagolewski, M., Bartoszuk, M., Cena, A. (2021). Are cluster validity measures (in)valid? Information Sciences, 581:620–636. DOI: 10.1016/j.ins.2021.10.004.

[18]

Gagolewski, M., Cena, A., Bartoszuk, M., Brzozowski, L. (2023). Clustering with minimum spanning trees: How good can it be? under review (preprint). URL: https://arxiv.org/pdf/2303.05679.pdf, DOI: 10.48550/arXiv.2303.05679.

[19]

Gagolewski, M., et al. (2020). Benchmark suite for clustering algorithms – version 1. URL: https://github.com/gagolews/clustering-benchmarks, DOI: 10.5281/zenodo.3815066.

[20]

Graves, D., Pedrycz, W. (2010). Kernel-based fuzzy clustering and fuzzy clustering: A comparative experimental study. Fuzzy Sets and Systems, 161:522–543. DOI: 10.1016/j.fss.2009.10.021.

[21]

Hubert, L., Arabie, P. (1985). Comparing partitions. Journal of Classification, 2(1):193–218. DOI: 10.1007/BF01908075.

[22]

Jain, A.K., Law, M.H.C. (2005). Data clustering: A user's dilemma. Lecture Notes in Computer Science, 3776:1–10.

[23]

Karypis, G., Han, E.H., Kumar, V. (1999). CHAMELEON: Hierarchical clustering using dynamic modeling. Computer, 32(8):68–75. DOI: 10.1109/2.781637.

[24]

Kobren, A., Monath, N., Krishnamurthy, A., McCallum, A. (2017). A hierarchical algorithm for extreme clustering. In: Proc. 23rd ACM SIGKDD'17, pp. 255–264. DOI: 10.1145/3097983.3098079.

[25]

Ling, R.F. (1973). A probability theory of cluster analysis. Journal of the American Statistical Association, 68(341):159–164.

[26]

March, W.B., Ram, P., Gray, A.G. (2010). Fast Euclidean minimum spanning tree: Algorithm, analysis, and applications. In: Proc. 16th ACM SIGKDD'10, pp. 603–612. DOI: 10.1145/1835804.1835882.

[27]

McInnes, L., Healy, J., Astels, S. (2017). hdbscan: Hierarchical density based clustering. The Journal of Open Source Software, 2(11):205. DOI: 10.21105/joss.00205.

[28]

Müller, A.C., Nowozin, S., Lampert, C.H. (2012). Information theoretic clustering using minimum spanning trees. In: Proc. German Conference on Pattern Recognition. URL: https://github.com/amueller/information-theoretic-mst.

[29]

Müllner, D. (2013). fastcluster: Fast hierarchical, agglomerative clustering routines for R and Python. Journal of Statistical Software, 53(9):1–18. DOI: 10.18637/jss.v053.i09.

[30]

Naidan, B., Boytsov, L., Malkov, Y., Novak, D. (2019). Non-metric space library (NMSLIB) manual, version 2.0. URL: https://github.com/nmslib/nmslib/blob/master/manual/latex/manual.pdf.

[31]

Olson, C.F. (1995). Parallel algorithms for hierarchical clustering. Parallel Computing, 21:1313–1325. DOI: 10.1016/0167-8191(95)00017-I.

[32]

Pedregosa, F., et al. (2011). Scikit-learn: Machine learning in Python. Journal of Machine Learning Research, 12(85):2825–2830. URL: http://jmlr.org/papers/v12/pedregosa11a.html.

[33]

Rezaei, M., Fränti, P. (2016). Set matching measures for external cluster validity. IEEE Transactions on Knowledge and Data Engineering, 28(8):2173–2186. DOI: 10.1109/TKDE.2016.2551240.

[34]

Ultsch, A. (2005). Clustering with SOM: U*C. In: Workshop on Self-Organizing Maps, pp. 75–82.

[35]

Vinh, N.X., Epps, J., Bailey, J. (2010). Information theoretic measures for clusterings comparison: Variants, properties, normalization and correction for chance. Journal of Machine Learning Research, 11(95):2837–2854. URL: http://jmlr.org/papers/v11/vinh10a.html.