Search results for "Organizing"
showing 10 items of 113 documents
Growing Hierarchical Self-organizing Maps and Statistical Distribution Models for Online Detection of Web Attacks
2013
In modern networks, HTTP clients communicate with web servers using request messages. By manipulating these messages attackers can collect confidential information from servers or even corrupt them. In this study, the approach based on anomaly detection is considered to find such attacks. For HTTP queries, feature matrices are obtained by applying an n-gram model, and, by learning on the basis of these matrices, growing hierarchical self-organizing maps are constructed. For HTTP headers, we employ statistical distribution models based on the lengths of header values and relative frequency of symbols. New requests received by the web-server are classified by using the maps and models obtaine…
Web mining based on Growing Hierarchical Self-Organizing Maps: Analysis of a real citizen web portal☆
2008
This work is focused on the usage analysis of a citizen web portal, Infoville XXI (http://www.infoville.es) by means of Self-Organizing Maps (SOM). In this paper, a variant of the classical SOM has been used, the so-called Growing Hierarchical SOM (GHSOM). The GHSOM is able to find an optimal architecture of the SOM in a few iterations. There are also other variants which allow to find an optimal architecture, but they tend to need a long time for training, especially in the case of complex data sets. Another relevant contribution of the paper is the new visualization of the patterns in the hierarchical structure. Results show that GHSOM is a powerful and versatile tool to extract relevant …
Behavior Classification with Self-Organizing Maps
2001
We describe a method that applies Self-Organizing Maps for direct clustering of spatio-temporal data. We use the method to evaluate the behavior of RoboCup players. By training the Self-Organizing Map with player data we have the possibility to identify various clusters representing typical agent behavior patterns. Thus we can draw certain conclusions about their tactical behavior, using purely motion data, i.e. logfile information. In addition, we examine the player-ball interaction that give information about the players' technical capabilities.
On the Optimization of Self-Organizing Maps by Genetic Algorithms
1999
Publisher Summary This chapter reviews the research on the genetic optimization of self-organizing maps (SOMs). The optimization of learning rule parameters and of initial weights is able to improve network performance. The latter, however, requires chromosome sizes proportional to the size of the SOM and becomes unwieldy for large networks. The optimization of learning rule structures leads to self-organization processes of character similar to the standard learning rule. A particularly strong potential lies in the optimization of SOM topologies, which allows the study of global dynamical properties of SOMs and related models, as well as to develop tools for their analysis. Hierarchies of …
Qualitative analysis of goat and sheep production data using self-organizing maps
2009
The aim of this study was to analyse the relationship between different small ruminant livestock production systems with different levels of specialization. The analysis is carried out by using the self-organizing map. This tool allows high-dimensional input spaces to be mapped into much lower-dimensional spaces, thus making it much more straightforward to understand any set of data. These representations enable the visual extraction of qualitative relationships among variables (visual data mining), converting the data to maps. The data used in this study were obtained from surveys completed by farmers who are principally dedicated to goat and sheep production. With the self-organizing map …
Simulated Annealing Technique for Fast Learning of SOM Networks
2011
The Self-Organizing Map (SOM) is a popular unsupervised neural network able to provide effective clustering and data visualization for multidimensional input datasets. In this paper, we present an application of the simulated annealing procedure to the SOM learning algorithm with the aim to obtain a fast learning and better performances in terms of quantization error. The proposed learning algorithm is called Fast Learning Self-Organized Map, and it does not affect the easiness of the basic learning algorithm of the standard SOM. The proposed learning algorithm also improves the quality of resulting maps by providing better clustering quality and topology preservation of input multi-dimensi…
Fast Training of Self Organizing Maps for the Visual Exploration of Molecular Compounds
2007
Visual exploration of scientific data in life science\ud area is a growing research field due to the large amount of\ud available data. The Kohonen’s Self Organizing Map (SOM) is\ud a widely used tool for visualization of multidimensional data.\ud In this paper we present a fast learning algorithm for SOMs\ud that uses a simulated annealing method to adapt the learning\ud parameters. The algorithm has been adopted in a data analysis\ud framework for the generation of similarity maps. Such maps\ud provide an effective tool for the visual exploration of large and\ud multi-dimensional input spaces. The approach has been applied\ud to data generated during the High Throughput Screening\ud of mo…
Automatic concept maps generation in support of educational processes
2014
A VLE is a system where three main actors can be devised: the teacher in the role of instructional designer, the tutor, and the stu- dent. Instructional designers need easy interaction for specifying the course domain structure to the system, and for controlling how well the learning materials agree to such a structure. Tutors need tools for having a holistic perception of the evolution of single students and/or groups in the VLE during the learning process. Finally, students need self regulation in terms of controlling their learning rate, reflect on their learning strategies, and comparing with other people in the class. In this work we claim that sharing an implicit representation of the…
Dynamical attractors of memristors and their networks
2018
It is shown that the time-averaged dynamics of memristors and their networks periodically driven by alternating-polarity pulses may converge to fixed-point attractors. Starting with a general memristive system model, we derive basic equations describing the fixed-point attractors and investigate attractors in the dynamics of ideal, threshold-type and second-order memristors, and memristive networks. A memristor potential function is introduced, and it is shown that in some cases the attractor identification problem can be mapped to the problem of potential function minimization. Importantly, the fixed-point attractors may only exist if the function describing the internal state dynamics dep…
From deterministic cellular automata to coupled map lattices
2016
A general mathematical method is presented for the systematic construction of coupled map lattices (CMLs) out of deterministic cellular automata (CAs). The entire CA rule space is addressed by means of a universal map for CAs that we have recently derived and that is not dependent on any freely adjustable parameters. The CMLs thus constructed are termed real-valued deterministic cellular automata (RDCA) and encompass all deterministic CAs in rule space in the asymptotic limit $\kappa \to 0$ of a continuous parameter $\kappa$. Thus, RDCAs generalize CAs in such a way that they constitute CMLs when $\kappa$ is finite and nonvanishing. In the limit $\kappa \to \infty$ all RDCAs are shown to ex…