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|Title:||Detecting Clusters in the Data from Variance Decompositions of Its Projections||Authors:||Yatracos, Yannis G.||Keywords:||Analysis of variance;Classification;Clusters;Data structures||Category:||Economics and Business||Field:||Social Sciences||Issue Date:||24-Jan-2013||Publisher:||Springer Verlag||Source:||Journal of Classification Volume 30, Issue 1, 2013, Pages 30-55||metadata.dc.doi:||10.1007/s00357-013-9124-9||Abstract:||A new projection-pursuit index is used to identify clusters and other structures in multivariate data. It is obtained from the variance decompositions of the data's one-dimensional projections, without assuming a model for the data or that the number of clusters is known. The index is affine invariant and successful with real and simulated data. A general result is obtained indicating that clusters' separation increases with the data's dimension. In simulations it is thus confirmed, as expected, that the performance of the index either improves or does not deteriorate when the data's dimension increases, making it especially useful for "large dimension-small sample size" data. The efficiency of this index will increase with the continuously improved computer technology. Several applications are presented.||URI:||http://ktisis.cut.ac.cy/handle/10488/9966||ISSN:||01764268||Rights:||© 2013 Springer Science+Business Media New York.||Type:||Article|
|Appears in Collections:||Άρθρα/Articles|
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