The collection of increasing amounts of data in health care has become relevant for pain
therapy and research. This poses problems for analyses with classical approaches, which is …

Currently, the development of medicines for complex diseases requires the development of
combination drug therapies. It is necessary because in many cases, one drug cannot target …

We present a study focused on constructing models of players for the major commercial title
Tomb Raider: Underworld (TRU). Emergent self-organizing maps are trained on high-level …

A new clustering algorithm based on emergent SOM is proposed. This algorithm, called U*
C, uses distance information together with density structures. No particular geometrical …

The explosion of data in the information age has provided an opportunity to explore the
possibility of characterizing the climate patterns using data mining techniques. Nigeria has a …

An overview on the usage of emergent self organizing maps is given. U-Maps visualize the
distance structures of high dimensional data sets. P-Maps show their density structures and …

The self-organizing map (SOM) is a powerful method for visualization, cluster extraction, and
data mining. It has been used successfully for data of high dimensionality and complexity …

Emergent self organizing feature maps (ESOM) may be regarded as self organized,
topology preserving projections of high dimensional data onto a two dimensional map. On …

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 …