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Interactive Data Exploration Using Mds Mapping
 5th Conf. on Neural Networks and Soft Computing
, 2000
"... Interactive exploratory data analysis can be realised by using dimensionality reduction techniques integrated in data visualization software. This work presents an adaptation of one multidimensional scaling algorithm to provide it with generalization capability, allowing the display of new data on a ..."
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Cited by 11 (5 self)
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Interactive exploratory data analysis can be realised by using dimensionality reduction techniques integrated in data visualization software. This work presents an adaptation of one multidimensional scaling algorithm to provide it with generalization capability, allowing the display of new data on an existing mapping. The ensuing relative mapping is used to help the understanding of classification results. Keywords: Data visualization, Exploratory Data Analysis, multidimensional scaling.
Neural and Statistical Methods for the Visualization of Multidimensional Data
 DISSERTATION, KATEDRA METOD KOMPUTEROWYCH UMK
, 2001
"... In many fields of engineering science we have to deal with multivariate numerical data. In order to choose the technique that is best suited to a given task, it is necessary to get an insight into the data and to "understand" them. Much information allowing the understanding of multivariat ..."
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Cited by 9 (2 self)
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In many fields of engineering science we have to deal with multivariate numerical data. In order to choose the technique that is best suited to a given task, it is necessary to get an insight into the data and to "understand" them. Much information allowing the understanding of multivariate data, that is the description of its global structure, the presence and shape of clusters or outliers, can be gained through data visualization. Multivariate data visualization can be realized through a reduction of the data dimensionality, which is often performed by mathematical and statistical tools that are well known. Such tools are Principal Components Analysis or Multidimensional Scaling. Artificial neural networks have developed and found applications mainly in the last two decades, and they are now considered as a mature field of research. This thesis investigates the use of existing algorithms as applied to multivariate data visualization. First an overview of existing neural and statistical techniques applied to data visualization is presented. Then a comparison is made between two chosen algorithms from the point of view of multivariate data visualization. The chosen neural network algorithm is Kohonen's SelfOrganizing Maps, and the statistical technique is Multidimensional Scaling. The advantages and drawbacks from the theoretical and practical viewpoints of both approaches are put into light. The preservation of data topology involved by those two mapping techniques is discussed. The multidimensional scaling method was analyzed in details, the importance of each parameter was determined, and the technique was implemented in metric and nonmetric versions. Improvements to the algorithm were proposed in order to increase the performance of the mapping process. A graphic...
K.: Improving Performance of SelfOrganising Maps with Distance Metric Learning Method
 In ICAISC(2012
"... Abstract. SelfOrganising Maps (SOM) are Artificial Neural Networks used in Pattern Recognition tasks. Their major advantage over other architectures is human readability of a model. However, they often gain poorer accuracy. Mostly used metric in SOM is the Euclidean distance, which is not the best ..."
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Abstract. SelfOrganising Maps (SOM) are Artificial Neural Networks used in Pattern Recognition tasks. Their major advantage over other architectures is human readability of a model. However, they often gain poorer accuracy. Mostly used metric in SOM is the Euclidean distance, which is not the best approach to some problems. In this paper, we study an impact of the metric change on the SOM’s performance in classification problems. In order to change the metric of the SOM we applied a distance metric learning method, socalled ’Large Margin Nearest Neighbour’. It computes the Mahalanobis matrix, which assures small distance between nearest neighbour points from the same class and separation of points belonging to different classes by large margin. Results are presented on several real data sets, containing for example recognition of written digits, spoken letters or faces.
Visualization of large data sets using MDS combined with LVQ.
"... www.phys.uni.torun.pl/kmk Abstract. A common task in data mining is the visualization of multivariate objects using various methods, allowing human observers to perceive subtle interrelations in the dataset. Multidimensional scaling (MDS) is a well known technique used for this purpose, but it due ..."
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www.phys.uni.torun.pl/kmk Abstract. A common task in data mining is the visualization of multivariate objects using various methods, allowing human observers to perceive subtle interrelations in the dataset. Multidimensional scaling (MDS) is a well known technique used for this purpose, but it due to its computational complexity there are limitations on the number of objects that can be displayed. Combining MDS with a clustering method as Learning Vector Quantization allows to obtain displays of large databases that preserve both high accuracy of clustering methods and good visualization properties. 1