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In exploratory data analysis of high dimensional data one Eof the main tasks is the formation of a simplified overview of data sets. Clustering and projection are among the examples of useful methods to achieve this task. However there are several types of data where the use of this measure is not adequate, such as the categorical data. In this paper we(More)
One of the most widely used clustering techniques used in GISc problems is the k-means algorithm. One of the most important issues in the correct use of k-means is the initialization procedure that ultimately determines which part of the solution space will be searched. In this paper we briefly review different initialization procedures, and propose(More)
In this paper we explore the advantages of using Self-Organized Maps (SOMs) when dealing with geo-referenced data. The standard SOM algorithm is presented, together with variants which are relevant in the context of the analysis of geo-referenced data. We present a new SOM architecture, the Geo-SOM, which was especially designed to take into account spatial(More)
Empirical results are presented concerning data fusion performed over several combinations of EEG/ECG channel readings of sport shooting athletes. Our purpose in applying different data fusion approaches was that of finding a satisfactory set of features, such that would allow us to build adequate classifiers on the data. The resulting data sets were used(More)
Genetic algorithms (GA) have been found to provide global near optimal solutions in a wide range of complex problems. In this paper genetic algorithms have been used to deal with the complex problem of zone design. The zone design problem comprises a large number of geographical tasks, from which electoral districting is probably the most well known. The(More)
Regionalization and uniform/homogeneous region building constitutes one of the most longstanding concerns of geographers. In this paper we explore the Geo-Self-Organizing Map (Geo-SOM) as a tool to develop homogeneous regions and perform geographic pattern detection. The Geo-SOM presents several a dvantages over other available methods. The possibility of "(More)
The basic idea of a cartogram is to distort a map. This distortion comes from the substitution of area for some other variable (in most examples population. The SOM constitutes a very flexible tool that has been used in many different tasks. In this article we have presented a general method for constructing density-equalizing projections or cartograms,(More)
The Self-Organizing Map (SOM) is an artificial neural network that performs simultaneously vector quantization and vector projection. Due to this characteristic, the SOM can be visualized through the output space, i.e. considering the vector projection perspective, and through the input data space, emphasizing the vector quantization process. Among all the(More)
– A method for planning routes for patrol vessels is proposed. This method is based on a Self-Organizing Map (SOM) solution for the Travelling Salesman Problem (TSP), although with significant changes. The locations of reported Search and Rescue (SAR) requests, together with the locations of reported occurances of illigal fishing activities are used as(More)
This paper presents a taxonomy for Self-organizing Maps (SOMs) for temporal sequence processing. Four main application areas for SOMs with temporal processing have been identified. These are prediction, control, monitoring and data mining. Three main techniques have been used to model temporal relations in SOMs: 1) pre-processing or post-processing the(More)