Cluster analysis in biomedical researches

Authors

  • A.S. Akopov Institute of General Pathology and Pathophysiology, RAMS, 8, Baltiyskaya str., 125315, Moscow, Russia
  • A.A. Moskovtsev Institute of General Pathology and Pathophysiology, RAMS, 8, Baltiyskaya str., 125315, Moscow, Russia; Russian Medical Academy of Postdegree Education, 2/1, Barrikadnaya str., 123836, Moscow, Russia
  • S.A. Dolenko Skobeltsyn Institute of Nuclear Physics, Moscow State University, 1, bld.2, Leninskye Gory, Moscow, GSP-1, 119991, Russia
  • G.D. Savina Russian Medical Academy of Postdegree Education, 2/1, Barrikadnaya str., 123836, Moscow, Russia

Keywords:

cluster analysis, multi-parameter statistics, cluster, k-means, hierarchical algorithms, artificial neural networks, Kohonen network

Abstract

Cluster analysis is one of the most popular methods for the analysis of multi-parameter data. The cluster analysis reveals the internal structure of the data, group the separate observations on the degree of their similarity. The review provides a definition of the basic concepts of cluster analysis, and discusses the most popular clustering algorithms: k-means, hierarchical algorithms, Kohonen networks algorithms. Examples are the use of these algorithms in biomedical research.

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Published

2013-11-29

Issue

Vol. 57 No. 4 (2013)

Section

Reviews

How to Cite

[1]
2013. Cluster analysis in biomedical researches. Patologicheskaya Fiziologiya i Eksperimental’naya Terapiya (Pathological physiology and experimental therapy). 57, 4 (Nov. 2013), 84–96.

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