Self organizing maps
Self -and super- organizing maps in R: the Kohonen package
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In this age of ever-increasing data set sizes, especially in the natural sciences, visualisation becomes more and more important. Self – organizing maps have many features that make them attractive in this respect: they do not rely on distributional assumptions, can handle
WEBSOM self – organizing maps of document collections
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Searching for relevant text documents has traditionally been based on keywords and Boolean expressions of them. Often the search results show high recall and low-precision, or vice versa. Considerable efforts have been made to develop alternative methods, but their
Self – Organizing Maps of Document Collections: A New Approach to Interactive Exploration.
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Powerful methods for interactive exploration and search from collections of free-form textual documents are needed to manage the ever-increasing flood of digital information. In this article we present a method, WEBSOM, for automatic organization of full-text document
Multiple self – organizing maps for intrusion detection
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While many techniques have been explored for detecting intrusive or abnormal behavior on computer systems, approaches that involve pattern matching, expert systems, and traditional neural networks require detectors to either be crafted by hand or trained upon examples of
Alternative ways for cluster visualization in self – organizing maps
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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
Self – organizing maps in natural language processing
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Abstract Kohonens Self – Organizing Map (SOM) is one of the most popular arti cial neural network algorithms. Word category maps are SOMs that have been organized according to word similarities, measured by the similarity of the short contexts of the words. Conceptually
Survey and comparison of quality measures for self – organizing maps
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Self – Organizing Maps have a wide range of beneficial properties for data mining, like vector quantization and projection. Several measures exist that quantify the quality of either of these properties. The scope of this work is to describe and compare some of the most well
Pattern discovery from stock time series using self – organizing maps
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Pattern discovery from time series is of fundamental importance. Particularly when the domain expert derived patterns do not exist or are not complete, an algorithm to discover specific patterns or shapes automatically from the time series data is necessary. Such an
Creating an order in digital libraries with self – organizing maps
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Formulation of suitable search expressions for information retrieval from large full-text databases currently require considerable e orts. Changing the scope of the search when, eg, too many or too few hits have been obtained, requires re-formulation of the search
NSOM: A real-time network-based intrusion detection system using self – organizing maps
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In this paper we describe an implementation of a network based Intrusion Detection System (IDS) using Self – Organizing Maps (SOM). The system uses a structured SOM to classify real- time Ethernet network data. A graphical tool continuously displays the clustered data to
Using psycho-acoustic models and self – organizing maps to create a hierarchical structuring of music by sound similarity
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With the advent of large musical archives the need to provide an organization of these archives becomes eminent. While artist-based organizations or title indexes help in locating a specific piece of music, a more intuitive, genre-based organization is required to
Credit card fraud detection using self – organizing maps
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Nowadays, credit card fraud detection is of great importance to financial institutions. This article presents an automated credit card fraud detection system based on the neural network technology. The authors apply the Self – Organizing Map algorithm to create a model A topographic map is a two-dimensional, nonlinear approximation of a potentially high- dimensional data manifold, which makes it an appealing instrument for visualizing and exploring high-dimensional data. The self-organizing map (SOM) is the most widely used
Kohonen self – organizing maps
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Neural networks are a fascinating concept. The first neural network, Perceptron , was created in 1956 by Frank Rosenblatt. Thirteen years later in 196 a publication known as Perceptrons, by Minsky and Papert, brought a devastating blow to neural network research
The architecture of emergent self – organizing maps to reduce projection errors.
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Emergent self – organizing maps (ESOM) be regarded as a non-linear projection technique using neurons arranged as a lattice embedded in a lowdimensional map space. The preservation of the topography of the high dimensional input data onto the map is a
Kohonen self – organizing maps : Is the normalization necessary
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The self – organizing algorithm of Kohonen is well known for its ability to map an input space with a neural network. According to multiple observations, self organization seems to be an essential feature of the brain. In this paper we focus on the distance measure used by the
Self – organizing maps of symbol strings with application to speech recognition
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Speech recognition based on phonemic units produces phoneme strings as output. These are usually not identical with the pronunciation models found in a conventional dictionary. Due to the coarticulation effects, the phones depend on their contexts. The produced phoneme strings
Limitations of self – organizing maps for vector quantization and multidimensional scaling
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The limitations of using self – organizing maps (SaM) for either clustering/vector quantization (VQ) or multidimensional scaling (MDS) are being discussed by reviewing recent empirical findings and the relevant theory. SaMs remaining ability of doing both VQ and MDS at the
Hyperbolic self – organizing maps for semantic navigation
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We introduce a new type of Self – Organizing Map (SOM) to navigate in the Semantic Space of large text collections. We propose a hyperbolic SOM (HSOM) based on a regular tesselation of the hyperbolic plane, which is a non-euclidean space characterized by
Visual analysis of self – organizing maps
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In the article, an additional visualization of self – organizing maps (SOM) has been investigated. The main objective of self – organizing maps is data clustering and their graphical presentation. Opportunities of SOM visualization in four systems (NeNet, SOM