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A dendrite method for cluster analysis

Caliński, T., Harabasz, J·1974·Communications in Statistics
DOI 10.1080/03610927408827101
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Abstract

A method for identifying clusters of points in a multidimensional Euclidean space is described and its application to taxonomy considered. It reconciles, in a sense, two different approaches to the investigation of the spatial relationships between the points, viz., the agglomerative and the divisive methods. A graph, the shortest dendrite of Florek etal. (1951a), is constructed on a nearest neighbour basis and then divided into clusters by applying the criterion of minimum within cluster sum of squares. This procedure ensures an effective reduction of the number of possible splits. The method may be applied to a dichotomous division, but is perfectly suitable also for a global division into any number of clusters. An informal indicator of the "best number" of clusters is suggested. It is a"variance ratio criterion" giving some insight into the structure of the points. The method is illustrated by three examples, one of which is original. The results obtained by the dendrite method are compared with those obtained by using the agglomerative method or Ward (1963) and the divisive method of Edwards and Cavalli-Sforza (1965).

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  • The optimal number of clusters was determined using the Calinski-Harabasz index and further verified by average silhouette width and prediction strength.

    “Next, we used the Calinski-Harabasz (CH) index [59] to determine the optimal number of clusters, and we further verified this by calculating the average silhouette width of the samples, which is a measure of the separation of samples within one cluster from those of another cluster, as well as the prediction strength, another measure of the efficiency of clustering.”

    From Citizen science charts two major “stomatotypes” in the oral microbiome of adolescents and reveals links with habits and drinking water composition (2018) · cites this paper as [59]

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No references could be resolved yet.

Research works printed but not linked: 19

  1. Bolshev , L.N . 1969 . “ Cluster analysis ” . In Proc. 37th Session of the International Statistical Institute Vol. 1 , 25 – 411 .
  2. Oaliiiski , T . 1969 . “ On the application of cluster analysis to experimental results ” . In Proc. 57th Session of the International Statistical Institute Vol. 2 , 10 – 108 .
  3. 10.2307/2528096 · DOI
  4. 10.4064/cm-2-3-4-282-285 · DOI
  5. Florek , K , Lukaszewicz , J , Perkal , J , Steinhaus , H and Zubrzycki , S . 1951b . Taksonomia wrockawska . Przeglad Antropologiczny , 17 : 193 – 211 .
  6. Fortier , J.J and Solomon , H . 1966 . “ Clustering procedures ” . In Multivariate Analysis , Edited by: Krishnaiah , P.R . 493 – 506 . New York : Academic Press .
  7. 10.1080/01621459.1967.10500923 · DOI
  8. 10.1093/biomet/53.3-4.325 · DOI
  9. Gower , J.C . 1966 . A comparison of some methods of cluster analysis . Biometrices , 23 : 37 – 623 .
  10. 10.2307/2346439 · DOI
  11. Ore , O . 1963 . Graphs and their Uses , New York : Random House, Inc .
  12. Perkal , J . 1953 . Taksonomia wroclawska . Przeglad Antropolo-gicgniy , 12 : 82 – 105 .
  13. Perkal , J . 1963 . Matematyka dla Prziyrodnlkdw i Rolników Warszawa Panstwowe Wydawnictwo Naukowe
  14. 1952 . Advanced Statistical Methods in Biometric Research , New York : John Wiley and Sons, Inc .
  15. Rao , C.R . 1964 . The use and interpretation of principal component analysis in applied research . Sankhya , A26 : 58 – 329 .
  16. Ross , G.J.S . 1969a . Minimum spanning tree . Appl. Statist , 18 : 4 – 103 . (Algorithm AS 13)
  17. Ross , G.J.S . 1969a . Printing the minimum spanning tree . Appl. Statist , 18 : 6 – 105 . Al-gorithm AS 14
  18. 10.1080/01621459.1963.10500845 · DOI
  19. Wishart , D. 5 new fortran IV programs for cluster analysis (CLUSTAN IA) , Kansas Geologic Computer Contributions .

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Metadata

Authors
Caliński, T., & Harabasz, J.
Venue
Communications in Statistics
Publisher
Informa UK Limited
Published
1974-01
Volume
3
Issue
1
Pages
1-27
ISSN
0090-3272
DOI
10.1080/03610927408827101
Cited by
4,480
References
0
Language
en

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