Abstract The Fundamental Clustering Problems Suite (FCPS) offers a variety of clustering
challenges that any algorithm should be able to handle given real-world data. The FCPS …

This open access book covers aspects of unsupervised machine learning used for
knowledge discovery in data science and introduces a data-driven approach to cluster …

Kohonen's self-organizing map (SOM) is a popular neural network architecture for solving
problems in the field of explorative data analysis, clustering, and data visualization. One of …

Seismic facies analysis aims to identify clusters (groups) of similar seismic trace shapes,
where each cluster can be considered to represent variability in lithology, rock properties …

The U-matrix has become a standard visualization of self-organizing feature maps (SOM).
Here we present the abstract U-matrix, which formalizes the structures on a U-matrix such …

This paper sheds some light on the differences between SOM and emergent SOM (ESOM).
The discussion in philosophy and epistemology about Emergence is summarized in the form …

Clustering problems arise in various domains of science and engineering. A large number of
methods have been developed to date. The Kohonen self-organizing map (SOM) is a …

Self-organizing map (SOM) is one of the most popular neural network methods for cluster
analysis. Clustering methods using SOM usually are two-stage procedures: first original data …

Multidimensional transfer functions can perform more sophisticated classification of
volumetric objects compared to 1-D transfer functions. However, visualizing and …

This work proposes a novel approach for the offline development and online implementation
of data-driven process monitoring (PM) using topological preservation techniques …