Search results for "Organizing"
showing 10 items of 113 documents
A New SOM Initialization Algorithm for Nonvectorial Data
2008
Self Organizing Maps (SOMs) are widely used mapping and clustering algorithms family. It is also well known that the performances of the maps in terms of quality of result and learning speed are strongly dependent from the neuron weights initialization. This drawback is common to all the SOM algorithms, and critical for a new SOM algorithm, the Median SOM (M-SOM), developed in order to map datasets characterized by a dissimilarity matrix. In this paper an initialization technique of M-SOM is proposed and compared to the initialization techniques proposed in the original paper. The results show that the proposed initialization technique assures faster learning and better performance in terms…
Analysis of Multi-Choice Questionnaires through Self-Organizing Maps
1998
This paper describes how Self-Organizing Maps can be used to analyse multi-choice gallups. In this method, the use of a single SOM for all available data is replaced with the use of multiple SOMs trained with subsets of gallup questions. The subgroupings located from these maps are then used to train a new concluding SOM that is more readable than any single SOM analysis would be.
RECURRENT SELF-ORGANIZATION OF SENSORY SIGNALS IN THE AUDITORY DOMAIN
2008
In this study, a psychoacoustical and connectionist modeling framework is proposed for the investigation of musical cognition. It is suggested that music perception involves the manipulation of 1) sensory representations that have correlations with psychoacoustical features of the stimulus, and 2) abstract representations of the statistical regularities underlying a particular musical syntax. In the implicit learning domain, sensory features have been shown to interact with the processes involved in the extraction of the regularities governing musical events combinations in a stream [e.g., 1]. Furthermore, in a more ecological context, it is well known that traditional Western tonal system …
Analysis of motor control and behavior in multi agent systems by means of artificial neural networks
2004
Abstract This article gives a short introduction to Self-Organizing Maps, a particular form of Artificial Neural Networks and shows by some examples, how these approaches can be used in order to analyze and visualize time series data originating from complex systems. The methods shown in this article have originally been developed for the analysis of RoboCup robot soccer games, a special kind of so-called Multi Agent Systems. Although this application has no direct connection to biomechanics, the examples shown here may give an impression of the abilities of Neural Networks in the field of Time Series Analysis in general. Because of the abstractness of the methods, it appears to be very lik…
Mapping Economic Activity in the European Union: Do Ownership, Industry and Location Matter?
2020
The paper proposes a new method for analysing the structure and dynamics of economic activity undertaken by locally owned and foreign-owned companies within the European Union. We employ an unsupervised learning algorithm that generates a neural network depicted on Kohonen maps and offering a clustering of companies with a different ownership (local and foreign) from various industries and countries of the European Union during 2009–2016. The research methodology, based on a self-organizing map (SOM) algorithm, belongs to a class of neural networks trained to organize data so that unknown patterns may be discovered, thus leading to results that cannot be attained by more traditional cluster…
Analysis of computer user behavior, security incidents and fraud using Self-Organizing Maps
2019
Abstract This paper addresses several topics of great interest in computer security in recent years: computer users’ behavior, security incidents and fraud exposure on the Internet, due to their high economic and social cost. Traditional research has been based mainly on gathering information about security incidents and fraud through surveys. The novelty of the present study is given by the use of Self-Organizing Maps (SOMs), a visual data mining technique. SOMs are applied to two data sets acquired using two different methodologies for collecting data about computer security. First, a traditional online survey about fraud exposure, security and user behavior was used. Second, in addition …
Stable Automatic Unsupervised Segmentation of Retinal Vessels Using Self-Organizing Maps and a Modified Fuzzy C-Means Clustering
2011
In this paper an automatic unsupervised method for the segmentation of retinal vessels is proposed. Three features are extracted from the tested image. The features are scaled down by a factor of 2 and mapped into a Self-Organizing Map. A modified Fuzzy C-Means clustering algorithm is used to divide the neuron units of the map in 2 classes. The entire image is again input for the Self-Organizing Map and the class of each pixel will be the class of its best matching unit in the Self-Organizing Map. Finally, the vessel network is post-processed using a hill climbing strategy on the connected components of the segmented image. The experimental evaluation on the DRIVE database shows accurate ex…
Automatic Unsupervised Segmentation of Retinal Vessels Using Self-Organizing Maps and K-Means Clustering
2011
In this paper an automatic unsupervised method for the segmentation of retinal vessels is proposed. A Self-Organizing Map is trained on a portion of the same image that is tested and K-means clustering algorithm is used to divide the map units in 2 classes. The entire image is again input for the Self-Organizing Map, and the class of each pixel will be the class of the best matching unit on the Self-Organizing Map. Finally, the vessel network is post-processed using a hill climbing strategy on the connected components of the segmented image. The experimental evaluation on the publicly available DRIVE database shows accurate extraction of vessels network and a good agreement between our segm…
Self-organizing maps: A new digital architecture
1991
An original hardware architecture implementing the self-organizing feature maps, which is one of the most powerful and efficent neural network algorithm, is presented. The architecture, contrary to the most investigated hardware implementations of neural networks, is a full digital one and it may be easily built by using the standard VLSI techniques.
Self-organizing maps could improve the classification of Spanish mutual funds
2006
In this paper, we apply nonlinear techniques (Self-Organizing Maps, k-nearest neighbors and the k-means algorithm) to evaluate the official Spanish mutual funds classification. The methodology that we propose allows us to identify which mutual funds are misclassified in the sense that they have historical performances which do not conform to the investment objectives established in their official category. According to this, we conclude that, on average, over 40% of mutual funds could be misclassified. Then, we propose an alternative classification, based on a double-step methodology, and we find that it achieves a significantly lower rate of misclassifications. The portfolios obtained from…