Mathematical Tools of Cluster Analysis ()
Abstract
The paper deals with cluster analysis and comparison of clustering methods. Cluster analysis belongs to multivariate statistical methods. Cluster analysis is defined as general logical technique, procedure, which allows clustering variable objects into groups-clusters on the basis of similarity or dissimilarity. Cluster analysis involves computational procedures, of which purpose is to reduce a set of data on several relatively homogenous groups-clusters, while the condition of reduction is maximal and simultaneously minimal similarity of clusters. Similarity of objects is studied by the degree of similarity (correlation coefficient and association coefficient) or the degree of dissimilarity-degree of distance (distance coefficient). Methods of cluster analysis are on the basis of clustering classified as hierarchical or non-hierarchical methods.
Share and Cite:
P. Trebuňa and J. Halčinová, "Mathematical Tools of Cluster Analysis,"
Applied Mathematics, Vol. 4 No. 5, 2013, pp. 814-816. doi:
10.4236/am.2013.45111.
Conflicts of Interest
The authors declare no conflicts of interest.
References
|
[1]
|
M. Palumbo, C. N. Lauro and M. J. Greenacre, “Data Analysis and Classification,” Springer, Berlin, 2010, p. 505. doi:10.1007/978-3-642-03739-9
|
|
[2]
|
L. Kaufmann, “Finding Groups in Data: An Introduction in Cluster Analysis,” Wiley, Hoboken, 2005, p. 342.
|
|
[3]
|
B. S. Everitt, S. Landau, M. Leese and D. Stahl, “Cluster Analysis,” Wiley, London, 2011, p. 348.
|
|
[4]
|
J. Bacher, A. Poge and K. Wenzig, “Clusteranalyse— Anwendungsorientierte Einfuhrung in Klassifikationsverfahren,” Oldenbourg, Munchen, 2010, p. 432.
doi:10.1524/9783486710236
|
|
[5]
|
J. Han and M. Kamber, “Data Mining—Concepts and Techniques,” MK Publisher, San Francisco, 2006, p. 772.
|
|
[6]
|
P. Trebuňa and J. Halcinová, “Experimental Modelling of the Cluster Analysis Processes,” Procedia Engineering, Vol. 48. 2012, pp. 673-678.
doi:10.1016/j.proeng.2012.09.569
|