Dieter Merkl

The growing hierarchical self-organizing map: exploratory analysis of high-dimensional data

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 relations of the data. With our novel growing hierarchical SOM (GHSOM) we address both limitations. The GHSOM is an artificial neural-network model with hierarchical architecture composed of independent growing SOMs. The motivation was to provide a model that adapts its architecture during its unsupervised training process according to the particular requirements of the input data. Furthermore, by providing a global orientation of the independently growing maps in the individual layers of the hierarchy, navigation across branches is facilitated. The benefits of this novel neural network are a problem-dependent architecture and the intuitive representation of hierarchical relations in the data. This is especially appealing in explorative data mining applications, allowing the inherent structure of the data to unfold in a highly intuitive fashion.

Content-based organization and visualization of music archives

With Islands of Music we present a system which facilitates exploration of music libraries without requiring manual genre classification. Given pieces of music in raw audio format we estimate their perceived sound similarities based on psychoacoustic models. Subsequently, the pieces are organized on a 2-dimensional map so that similar pieces are located close to each other. A visualization using a metaphor of geographic maps provides an intuitive interface where islands resemble genres or styles of music. We demonstrate the approach using a collection of 359 pieces of music.

The growing hierarchical self-organizing map

We present the growing hierarchical self-organizing map. This dynamically growing neural network model evolves into a hierarchical structure according to the requirements of the input data during an unsupervised training process. We demonstrate the benefits of this novel neural network model by organizing a real-world document collection according to their similarities.

Using Smoothed Data Histograms for Cluster Visualization in Self-Organizing Maps

Several methods to visualize clusters in high-dimensional data sets using the Self-Organizing Map (SOM) have been proposed. However, most of these methods only focus on the information extracted from the model vectors of the SOM. This paper introduces a novel method to visualize the clusters of a SOM based on smoothed data histograms. The method is illustrated using a simple 2-dimensional data set and similarities to other SOM based visualizations and to the posterior probability distribution of the Generative Topographic Mapping are discussed. Furthermore, the method is evaluated on a real world data set consisting of pieces of music.

Uncovering hierarchical structure in data using the growing hierarchical self-organizing map

Abstract Discovering the inherent structure in data has become one of the major challenges in data mining applications. It requires stable and adaptive models that are capable of handling the typically very high-dimensional feature spaces. In particular, the representation of hierarchical relations and intuitively visible cluster boundaries are essential for a wide range of data mining applications. Current approaches based on neural networks hardly fulfill these requirements within a single model. In this paper we present the growing hierarchical self-organizing map ( GHSOM ), a neural network model based on the self-organizing map. The main feature of this novel architecture is its capability of growing both in terms of map size as well as in a three-dimensional tree-structure in order to represent the hierarchical structure present in a data collection during an unsupervised training process. This capability, combined with the stability of the self-organizing map for high-dimensional feature space representation, makes it an ideal tool for data analysis and exploration. We demonstrate the potential of the GHSOM with an application from the information retrieval domain, which is prototypical both of the high-dimensional feature spaces frequently encountered in todays applications as well as of the hierarchical nature of data.

Text classification with self-organizing maps: Some lessons learned

Abstract The self-organizing map has already found appreciation for document classification in the information retrieval community. The map display is a highly effective and intuitive metaphor for orientation in the information space established by a document collection. In this paper we discuss ways for using self-organizing maps for document classification. Furthermore, we argue in favor of paying more attention to the fact that document collections lend themselves naturally to a hierarchical structure defined by the subject matter of the documents. We take advantage of this fact by using a hierarchically organized neural network, built up from a number of independent self-organizing maps in order to enable the true establishment of a document taxonomy. As a highly convenient side effect of using such an architecture, the time needed for training is reduced substantially and the user is provided with an even more intuitive metaphor for visualization. Since the single layers of self-organizing maps represent different aspects of the document collection at different levels of detail, the neural network shows the document collection in a form comparable to an atlas where the user may easily select the most appropriate degree of granularity depending on the actual focus of interest during the exploration of the document collection.

The SOM-enhanced JukeBox: Organization and Visualization of Music Collections Based on Perceptual Models

The availability of large music repositories calls for new ways of automatically organizing and accessing them. While artist-based listings or title indexes may help in locating a specific piece of music, a more intuitive, genre-based organization is required to allow users to browse an archive and explore its contents. So far, however, these organizations following musical styles have to be designed manually. With the SOM-enhanced JukeBox (SOMeJB) we propose an approach to automatically create an organization of music archives following their perceived sound similarity. More specifically, characteristics of frequency spectra are extracted and transformed according to psychoacoustic models. The resulting psychoacoustic Rhythm Patterns are further organized using the Growing Hierarchical Self-Organizing Map, an unsupervised neural network. On top of this advanced visualizations including Islands of Music (IoM) and Weather Charts offer an interface for interactive exploration of large music repositories.

The SOMLib Digital Library System

Digital Libraries have gained tremendous interest with several research projects addressing the wealth of challenges in this field. While computational intelligence systems are being used for specific tasks in this arena, the majority of projects relies on conventional techniques for the basic structure of the library itself. With the SOMLib project we created a digital library system that uses a neural network-based core for the representation of the library. The self-organizing map, a popular unsupervised neural network model, is used to topically structure a document collection similar to the organization of real-world libraries. Based on this core, additional modules provide information retrieval features, integrate distributed libraries, and automatically label the various topical sections in the document collection. A metaphor graphics based interface further assists the user in intuitively understanding the library providing an instant overview.

Exploration of text collections with hierarchical feature maps

Document classification is one of the central issues in information retrieval research. The aim is to uncover similarities between text documents. In other words, classification techniques are used to gain insight in the structure of the various data items contained in the text archive. In this paper we show the results from using a hierarchy of self-organizing maps to perform the text classification task. Each of the individual self-organizing maps is trained independently and gets specialized to a subset of the input data. As a consequence, the choice of this particular artificial neural network model enables the tme establishment of a document taxonomy. The benefit of this approach is a straightforward representation of document similarities combkd with drantatically reduced training time. In particular, the hierarchical representation of document collections is appealing because it is the underlying organizational principle in use by librarians providing the necessary familiarity for the user. The massive reduction in the time needed to train the artificial neural network together with its highly accurate clustering remdts makea it a challenging alternative to conventional approaches.

Clinical gait analysis by neural networks: issues and experiences

Clinical gait analysis is an area aimed at the provision of support for diagnoses and therapy considerations, the development of bio-feedback systems to train patients, and the recognition of effects of multiple diseases and still active compensation. The data recorded with ground reaction force measurement platforms is a convenient starting point for gait analysis. The authors argue in favor of using the raw data from such force platforms and apply artificial neural networks for gait malfunction identification. They discuss their latest results in this line of research by using a supervised learning rule. The employed classification approach is learning vector quantization which proved to be highly robust in the training process yielding a remarkably high recognition accuracy of gait patterns.