We present two enhanced visualization techniques for the self-organizing map allowing the intuitive representation of input data similarity. The general idea of both approaches is to visualize the relationship of nodes to facilitate the detection of cluster boundaries without modifying the architecture or the basic training process of SOM. One approach mirrors the movement of weight vectors dur...

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 popular tool that maps a high-dimensional space onto a small number of dimensions by placing similar elements close together, forming clusters. Cluster analysis is often left to the user. In this paper we present the method Tr...

Content-based video navigation is an efficient method for browsing video information. A common approach is to cluster shots into groups and visualize them afterwards. In this paper, we present a prototype that follows in general this approach. Unlike the existing systems, the clustering is based on a growing self-organizing map algorithm. We focus on studying the applicability of SOMs for video...

Semantic multimedia organization is an open challenge. In this chapter, we present an innovative way of automatically organizing multimedia information to facilitate content-based browsing. It is based on self-organizing maps. The visualization capabilities of the self-organizing map provide an intuitive way of representing the distribution of data as well as the object similarities. The main i...

Abstract Identifying community structure is an important issue in network science and has attracted attention of researchers in many fields. It is relevant for social tasks, biological inquires, and technological problems. In this paper, we proposed a new approach based on self-organizing map to community detection. By using a proper weight-updating scheme, a network can be organized into dense...

The Growing Self Organizing Map (GSOM) is a dynamic variant of the Self Organizing Map (SOM). It has been mainly used on low dimensional data sets. In this paper the GSOM is applied on high dimensional data sets and its performance is evaluated. Several modifications to the original GSOM algorithm are presented that enable the GSOM to be applied on high dimensional data .The modified version of...

The usual first step in analyzing the large and high-dimensional data sets measured by microarrays is visual exploration. In this work self-organizing maps have been used to visualize similarity relationships of data samples. In all unsupervised data analysis methods the measure of similarity determines the result; we propose to use the learning metrics principle to derive a metric from interre...

Neural maps project data from an input space onto a neuron position in a (often lower dimensional) output space grid in a neighborhood preserving way, with neighboring neurons in the output space responding to neighboring data points in the input space. A map-learning algorithm can achieve an optimal neighborhood preservation only, if the output space topology roughly matches the effective stru...