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The self-organizing map (SOM) is a very popular unsupervised neural-network model for the analysis of high-dimensional input data as in data mining applications. However, at least two limitations have to be noted, which are related to the static architecture of this model as well as to the limited capabilities for the representation of hierarchical(More)
In recent years a technological and sociological paradigm shift has taken place in the Internet that is often referred to as Web 2.0. Companies and individuals have started to adapt existing Web sites to the new standards and principles and created new types of Web services and communities. The tourism domain is no exception to this trend-new tourism(More)
The huge text archives and retrieval systems of legal information have not achieved yet the representation in the well-known subject-oriented structure of legal commentaries. Content-based classification and text analysis remains a high priority research topic. In the joint KONTERM, SOM and LabelSOM projects, learning techniques of neural networks are used(More)
With the rising popularity of digital music archives the need for new access methods such as interactive exploration or similarity-based search become significant. In this paper we present the PlaySOM, as well as the Pock-etSOMPlayer, two novel interfaces allowing to browse a music collection by navigating a map of clustered music tracks and to select(More)
The Self-Organizing Map is a popular neural network model for data analysis, for which a wide variety of visualization techniques exists. We present a novel technique that takes the density of the data into account. Our method defines graphs resulting from nearest neighbor-and radius-based distance calculations in data space and shows projections of these(More)
Self-Organizing Maps have been applied in various industrial applications and have proven to be a valuable data mining tool. In order to fully benefit from their potential, advanced visualization techniques assist the user in analyzing and interpreting the maps. We propose two new methods for depicting the SOM based on vector fields, namely the Gradient(More)