PolSOM : A new method for multidimensional data visualization

Research output: Journal Publications and Reviews (RGC: 21, 22, 62)21_Publication in refereed journalpeer-review

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Detail(s)

Original languageEnglish
Pages (from-to)1668-1675
Journal / PublicationPattern Recognition
Volume43
Issue number4
Publication statusPublished - Apr 2010

Abstract

In this paper, a new algorithm named polar self-organizing map (PolSOM) is proposed. PolSOM is constructed on a 2-D polar map with two variables, radius and angle, which represent data weight and feature, respectively. Compared with the traditional algorithms projecting data on a Cartesian map by using the Euclidian distance as the only variable, PolSOM not only preserves the data topology and the inter-neuron distance, it also visualizes the differences among clusters in terms of weight and feature. In PolSOM, the visualization map is divided into tori and circular sectors by radial and angular coordinates, and neurons are set on the boundary intersections of circular sectors and tori as benchmarks to attract the data with the similar attributes. Every datum is projected on the map with the polar coordinates which are trained towards the winning neuron. As a result, similar data group together, and data characteristics are reflected by their positions on the map. The simulations and comparisons with Sammon's mapping, SOM and ViSOM are provided based on four data sets. The results demonstrate the effectiveness of the PolSOM algorithm for multidimensional data visualization. © 2009 Elsevier Ltd. All rights reserved.

Research Area(s)

  • Clustering, Nonlinear mapping, Self-organizing map (SOM), Visualization, Visualization-induced SOM (ViSOM)

Citation Format(s)

PolSOM: A new method for multidimensional data visualization. / Xu, Lu; Xu, Yang; Chow, Tommy W.S.
In: Pattern Recognition, Vol. 43, No. 4, 04.2010, p. 1668-1675.

Research output: Journal Publications and Reviews (RGC: 21, 22, 62)21_Publication in refereed journalpeer-review