Textual description of the concept plays an important role in the conceptual model comprehensibility. In this paper, the self-organizing map to test the model comprehensibility is suggested. An explanatory text of the concept is transformed into a numerical vector. Several vector spaces are build using the hyponymy of the concepts from the WordNet dictionary. The received conceptual model vecto...

We present a time domain approach to explore a sound transformation paradigm for musical performance. Given a set of sounds containing a priori desired qualities and a population of agents interacting locally, the method generates both musical form and matter resulting from sonic trajectories. This proposal involves the use of bio-inspired algorithms, which possess intrinsic features of adaptiv...

Among the large number of research publications discussing the SOM (Self-Organizing Map) [1, 2, 18, 19] different variants and extensions have been introduced. One of the SOM based models is the Growing Hierarchical Self-Organizing Map (GHSOM) [3-6]. The GHSOM is a neural architecture combining the advantages of two principal extensions of the self-organizing map, dynamic growth and hierarchica...

Abstract In this study, we present self-organizing maps and discuss their role in the analysis and visualization of software modules in the space of software measures. We reveal how self-organizing maps create a user-friendly and interactive visualization tool that helps user/software designer inspect various alternatives and get a thorough insight into the structure of the clusters of the soft...

Discovering the inherent structure in data has become one of the major challenges in data mining applications. It requires the development of stable and adaptive models that are capable of handling the typically very high-dimensional feature spaces. In this paper we present the Growing Hierarchical Self-Organizing Map (GH-SOM), a neural network model based on the self-organizing map. The main f...

This paper proposes a novel approach for a Constructive Self-Organizing Map (SOM) based world modeling for search and rescue operations in disaster environments. In our approach, nodes of the self organizing network consist of victim and waypoint classes where victim denotes a human being waiting to be rescued and waypoint denotes a free space that can be reached from the entrance of debris. Th...

We review a new form of self-organizing map which is based on a nonlinear projection of latent points into data space, identical to that performed in the Generative Topographic Mapping (GTM) [1]. But whereas the GTM is an extension of a mixture of experts, this model is an extension of a product of experts [6]. We show visualisation and clustering results on a data set composed of video data of...

Kohonen’s Self-Organizing Map is a popular method which allows the projection of high dimensional data onto a low dimensional display space. Models of Self-Organizing Maps for the treatment of graphs have also been defined and studied. This paper proposes an extension to the GraphSOM model which substantially improves the stability of the model, and, as a side effect, allows for an acceleration...