Abstract The Self-Organizing Maps (SOM) is a very popular algorithm, introduced by Teuvo
Kohonen in the early 80s. It acts as a non supervised clustering algorithm as well as a …

Abstract The Self-Organizing Map (SOM) is widely used, easy to implement, has nice
properties for data mining by providing both clustering and visual representation. It acts as …

Among the variety of algorithms that have been developed for clustering, prototype-based
approaches are very popular due to their low computational complexity, allowing real-life …

Self-organizing maps (SOM) are a useful tool for exploring data. In its original version, the
SOM algorithm was designed for numerical vectors. Since then, several extensions have …

Graphs have attracted a burst of attention in the last years, with applications to social
science, biology, computer science... In the present paper, we illustrate how self-organizing …

This research aims to develop kernel GNG, a kernelized version of the growing neural gas
(GNG) algorithm, and to investigate the features of the networks generated by the kernel …

Data clustering is a very important and challenging task in Artificial Intelligence (AI) field with
many applications such as bio-informatics, medical, enhancing recommendation engines or …

Advances in new sequencing techniques and the diversification in protocols now make it
possible to study an organism at different biological scales. The latter are used to study the …

Self-Organizing Maps (SOM)[] are a popular clustering and visualization algorithm. Several
implementations of the SOM algorithm exist in different mathematical/statistical softwares …

Analyzing non vectorial data has become a common trend in a number of real-life
applications. Various prototype-based methods have been extended to answer this need by …