This article describes the implementation of a system that is able to organize vast document collections according to textual similarities. It is based on the Self-Organizing Map (SOM) algorithm. As the feature vectors for the documents we use statistical representations of their vocabularies. The main goal in our work has been to scale up the SOM algorithm to be able to deal with large amounts of high-dimensional data. In a practical experiment we mapped 6,840,568 patent abstracts onto a 1,002,240-node SOM. As the feature vectors we used 500-dimensional vectors of stochastic figures obtained as random projections of weighted word histograms. Keywords Data mining, exploratory data analysis, knowledge discovery, large databases, parallel implementation, random projection, Self-Organizing Map (SOM), textual documents. I. Introduction A. From simple searches to browsing of self-organized data collections Locating documents on the basis of keywords and simple search expressions is a c...

: Learning Vector Quantization (LVQ) is a group of algorithms applicable to statistical pattern recognition, in which the classes are described by a relatively small number of codebook vectors, properly placed within each zone such that the decision borders are approximated by the nearest-neighbor rule. The LVQ PAK program package contains all programs necessary for the correct application of certain Learning Vector Quantization algorithms in an arbitrary statistical classification or pattern recognition task, as well as a program for the monitoring of the codebook vectors at any time during the learning process. The first version 1.0 of this program package was published in 1991 and since then the package has been updated regularly to include latest improvements in the LVQ implementations. This report that contains the last documentation was prepared for bibliographical purposes. Contents 1 Introduction 4 1.1 Contents of this package : : : : : : : : : : : : : : : : : : : : : : : :...

This work presents experiments to recognize pattern sequences using hidden Markov models (HMMs). The pattern sequences in the experiments are computed from speech signals and the recognition task is to decode the corresponding phoneme sequences. The training of the HMMs of the phonemes using the collected speech samples is a difficult task because of the natural variation in the speech. Two neural computing paradigms, the Self-Organizing Map (SOM) and the Learning Vector Quantization (LVQ) are used in the experiments to improve the recognition performance of the models. A HMM consists of sequential states which are trained to model the feature changes in the signal produced during the modeled process. The output densities applied in this work are mixtures of Gaussian density functions. SOMs are applied to initialize and train the mixtures to give a smooth and faithful presentation of the feature vector space defined by the corresponding training samples. The SOM maps similar feature vect...

Due to efficient compression algorithms like MP3, the number and size of digital music repositories have increased dramatically over the past few years. Consequently, the demand for obtaining digital music from the Internet has been rising. Hence, effective methods for finding pieces of music in such repositories are becoming more and more important. Unfortunately, when working with traditional user interfaces which solely provide text-based search, the user already has to know certain textual properties of the songs he/she is looking for (e.g. name of the artist or album). In contrast, the prototype of the user interface which has been developed by the author is based on graphical visualizations of musical similarities between the pieces contained in the repository. This enables the user to exploratively browse through the collection, an approach which is especially useful for discovering formerly unknown pieces of music. In order to provide different views of the music collection, two algorithms were chosen to process the audio signals. These algorithms measure musical similarities according to rhythmic and timbral properties. The developed user interface “ViSMuC ” (Visualization of Structured Music Collections) implements an artificial intelligence method called Aligned Self-Organizing Maps in which high-dimensional data is represented by a 2-dimensional map. The pieces of music are visualized according to an adjustable weighting of their rhythmic

The Self-Organizing Map (SOM) is a powerful tool for exploratory data analysis which has been employed in a wide range of data mining applications. We present a novel approach...

this documentation are stored in the directory /pub/lvq pak. The files are in multiple formats to ease their downloading and compiling. The directory /pub/lvq pak contains the following files: