Investigation of Proportional Link Linkage Clustering Methods

W. Day, H. Edelsbrunner, Journal of Classification 2 (1985) 239–254.

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Abstract
Proportional link linkage (PLL) clustering methods are a parametric family of monotone invariant agglomerative hierarchical clustering methods. This family includes the single, minimedian, and complete linkage clustering methods as special cases; its members are used in psychological and ecological applications. Since the literature on clustering space distortion is oriented to quantitative input data, we adapt its basic concepts to input data with only ordinal significance and analyze the space distortion properties of PLL methods. To enable PLL methods to be used when the numbern of objects being clustered is large, we describe an efficient PLL algorithm that operates inO(n 2 logn) time andO(n 2) space
Publishing Year
Date Published
1985-12-01
Journal Title
Journal of Classification
Acknowledgement
This work was partially supported by the Natural Sciences and Engineering Research Council of Canada and by the Austrian Fonds zur Förderung der wissenschaftlichen Forschung.
Volume
2
Issue
2-3
Page
239 - 254
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Day W, Edelsbrunner H. Investigation of Proportional Link Linkage Clustering Methods. Journal of Classification. 1985;2(2-3):239-254. doi:10.1007/BF01908077
Day, W., & Edelsbrunner, H. (1985). Investigation of Proportional Link Linkage Clustering Methods. Journal of Classification, 2(2–3), 239–254. https://doi.org/10.1007/BF01908077
Day, William, and Herbert Edelsbrunner. “Investigation of Proportional Link Linkage Clustering Methods.” Journal of Classification 2, no. 2–3 (1985): 239–54. https://doi.org/10.1007/BF01908077.
W. Day and H. Edelsbrunner, “Investigation of Proportional Link Linkage Clustering Methods,” Journal of Classification, vol. 2, no. 2–3, pp. 239–254, 1985.
Day W, Edelsbrunner H. 1985. Investigation of Proportional Link Linkage Clustering Methods. Journal of Classification. 2(2–3), 239–254.
Day, William, and Herbert Edelsbrunner. “Investigation of Proportional Link Linkage Clustering Methods.” Journal of Classification, vol. 2, no. 2–3, Springer, 1985, pp. 239–54, doi:10.1007/BF01908077.

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