Algorithms for the visualization of large and multivariate data sets

Authors:
Friedhelm Schwenker;Hans A. Kestler;Günther Palm
Affiliations:
Univ. of Ulm, Germany;Univ. of Ulm, Germany;Univ. of Ulm, Germany
Venue:
Self-Organizing neural networks
Year:
2001

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Abstract

In this chapter we discuss algorithms for clustering and visualization of large and multivariate data. We describe an algorithm for exploratory data analysis which combines adaptive c-means clustering and multi-dimensional scaling (ACMDS). ACMDS is an algorithm for the online visualization of clustering processes and may be considered as an alternative approach to Kohonen's self organizing feature map (SOM). Whereas SOM is a heuristic neural network algorithm, ACMDS is derived from multivariate statistical algorithms. The implications of ACMMDS are illustrated through five different data sets.