KMC/EDAM: A New Approach for the Visualization of K-Means Clustering Results

Abstract

Abstract. In this work we introduce a method for classification and visualization. In contrast to simultaneous methods like e.g. Kohonen SOM this new approach, called KMC/EDAM, runs through two stages. In the first stage the data is clustered by classical methods like K-means clustering. In the second stage the centroids of the obtained clusters are visualized in a fixed target space which is directly comparable to that of SOM.

Data Analysis and Decision Support

“…The function EDAM (Eight Directions Arranged Map) computes a distance-based two-dimensional representation of the data in a rectangular grid known from Self-Organizing Maps (Raabe et al (2004)). The result of EDAM for the last cycle is shown in Figure 3, visualized by the function shardsplot.…”

Novel Two-Dimensional Visualization Approaches for Multivariate Centroids of Clustering Algorithms

“…Nitsche has used this type of visualization for document clustering [9], and Wagh et al have used it for a molecular biological dataset [10]. e third approach is the visualization of the dataset clustered by K-means in conjunction with an approach like neighborhood method location in the self-organizing map (SOM) technique, used by Raabe et al to implement a new approach to show K-means clustering with a novel technique locating the centroids on a 2D interface [11]. SOM is a successful mapping and clustering algorithm; nevertheless, it relies on the map-size parameter as the cluster number, and if it runs with a small map-size value, all centroids of the clusters are located contiguously, not at their original distances according to the high-dimensional structure.…”