Algorithms for the Visualization of Large and Multivariate Data Sets
Friedhelm Schwenker, Hans A. Kestler, Günther Palm · Studies in fuzziness and soft computing · 2002
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 a daptive c - m eans clustering and m ulti- d imensional 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 ACMDS are illustrated through five different data sets. These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.