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Self Organization of a Massive Document Collection
 IEEE Transactions on Neural Networks
"... This article describes the implementation of a system that is able to organize vast document collections according to textual similarities. It is based on the SelfOrganizing Map (SOM) algorithm. As the feature vectors for the documents we use statistical representations of their vocabularies. The m ..."
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Cited by 204 (14 self)
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This article describes the implementation of a system that is able to organize vast document collections according to textual similarities. It is based on the SelfOrganizing Map (SOM) algorithm. As the feature vectors for the documents we use statistical representations of their vocabularies. The main goal in our work has been to scale up the SOM algorithm to be able to deal with large amounts of highdimensional data. In a practical experiment we mapped 6,840,568 patent abstracts onto a 1,002,240node SOM. As the feature vectors we used 500dimensional vectors of stochastic figures obtained as random projections of weighted word histograms. Keywords Data mining, exploratory data analysis, knowledge discovery, large databases, parallel implementation, random projection, SelfOrganizing Map (SOM), textual documents. I. Introduction A. From simple searches to browsing of selforganized data collections Locating documents on the basis of keywords and simple search expressions is a c...
Clustering of the SelfOrganizing Map
, 2000
"... The selforganizing map (SOM) is an excellent tool in exploratory phase of data mining. It projects input space on prototypes of a lowdimensional regular grid that can be effectively utilized to visualize and explore properties of the data. When the number of SOM units is large, to facilitate quant ..."
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Cited by 159 (1 self)
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The selforganizing map (SOM) is an excellent tool in exploratory phase of data mining. It projects input space on prototypes of a lowdimensional regular grid that can be effectively utilized to visualize and explore properties of the data. When the number of SOM units is large, to facilitate quantitative analysis of the map and the data, similar units need to be grouped, i.e., clustered. In this paper, different approaches to clustering of the SOM are considered. In particular, the use of hierarchical agglomerative clustering and partitive clustering usingmeans are investigated. The twostage procedurefirst using SOM to produce the prototypes that are then clustered in the second stageis found to perform well when compared with direct clustering of the data and to reduce the computation time.
Neural Maps in Remote Sensing Image Analysis
 Neural Networks
, 2003
"... We study the application of SelfOrganizing Maps for the analyses of remote sensing spectral images. Advanced airborne and satellitebased imaging spectrometers produce very highdimensional spectral signatures that provide key information to many scientific inves tigations about the surface and at ..."
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Cited by 15 (12 self)
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We study the application of SelfOrganizing Maps for the analyses of remote sensing spectral images. Advanced airborne and satellitebased imaging spectrometers produce very highdimensional spectral signatures that provide key information to many scientific inves tigations about the surface and atmosphere of Earth and other planets. These new, so phisticated data demand new and advanced approaches to cluster detection, visualization, and supervised classification. In this article we concentrate on the issue of faithful topo logical mapping in order to avoid false interpretations of cluster maps created by an SaM. We describe several new extensions of the standard SaM, developed in the past few years: the Growing SelfOrganizing Map, magnification control, and Generalized Relevance Learn ing Vector Quantization, and demonstrate their effect on both lowdimensional traditional multispectral imagery and 200dimensional hyperspectral imagery.
Magnification control in selforganizing maps and neural gas, Neural Computation 18
, 2006
"... We consider different ways to control the magnification in selforganizing maps (SOM) and neural gas (NG). Starting from early approaches of magnification control in vector quantization, we then concentrate on different approaches for SOM and NG. We show that three structurally similar approaches ca ..."
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Cited by 8 (5 self)
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We consider different ways to control the magnification in selforganizing maps (SOM) and neural gas (NG). Starting from early approaches of magnification control in vector quantization, we then concentrate on different approaches for SOM and NG. We show that three structurally similar approaches can be applied to both algorithms: localized learning, concaveconvex learning, and winner relaxing learning. Thereby, the approach of concaveconvex learning in SOM is extended to a more general description, whereas the concaveconvex learning for NG is new. In general, the control mechanisms generate only slightly different behavior comparing both neural algorithms. However, we emphasize that the NG results are valid for any data dimension, whereas in the SOM case the results hold only for the onedimensional case. 1
On The Use Of SelfOrganizing Maps To Accelerate Vector Quantization
, 2004
"... SelforganiyO maps (SOM) arewiORV used forthei topologypreservatiV property:neier borie iie vectors arequantiS: (or classiJOP eiass on the samelocatiy or onneiyWJV ones on a prede#nedgrie SOM are alsowioOR used forthei moreclassiWV vectorquantiT#VSO property. We showi thi paper that usiO SOMiMOR#W o ..."
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Cited by 7 (4 self)
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SelforganiyO maps (SOM) arewiORV used forthei topologypreservatiV property:neier borie iie vectors arequantiS: (or classiJOP eiass on the samelocatiy or onneiyWJV ones on a prede#nedgrie SOM are alsowioOR used forthei moreclassiWV vectorquantiT#VSO property. We showi thi paper that usiO SOMiMOR#W of the moreclassiW; sissi competiOJJ learniJ (SCL)algori#; drasti;W;O irasti; the speed of convergence of the vector quantiJJJST process.Thi facti demonstrated throughextensiR sinsiRT:J onarti;;VO and real examples,wim specie SOM (#xed and decreasiP neieasiPRVR: and SCLalgoriWOPR 2003Elsevi: B.V. AllriOVW reserved. Keywords: SelforganiyOiyO Vector quantiy:WJOP Convergence speed;AcceleratiO 1. M3634R13 quanti;yROP (VQ)i awiRSW used tooli many dataanalysi# #elds. It consiJJ i replaciy acontiOPTW ditiOPTWJT by a#ni: set ofquantiOPTW whin min min a prede#ned die#nedOV cri#nedOV VectorquantiWy:JO may be usedi clusteriO or classiP#JSWO tasks, where the aii todetermiO groups (clusters) of data sharih commonproperti:W It can also be usedi datacompressiP: where the aii Correspondii author. Tel.: +3210472551; fax: +3210472598.
Explicit magnification control of selforganizing maps for ‘forbidden’ data
 IEEE Trans. Neural Net
, 2007
"... Abstract—In this paper, we examine the scope of validity of the explicit selforganizing map (SOM) magnification control scheme of Bauer et al. (1996) on data for which the theory does not guarantee success, namely data that are ndimensional, n 2, and whose components in the different dimensions ar ..."
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Cited by 7 (5 self)
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Abstract—In this paper, we examine the scope of validity of the explicit selforganizing map (SOM) magnification control scheme of Bauer et al. (1996) on data for which the theory does not guarantee success, namely data that are ndimensional, n 2, and whose components in the different dimensions are not statistically independent. The Bauer et al. algorithm is very attractive for the possibility of faithful representation of the probability density function (pdf) of a data manifold, or for discovery of rare events, among other properties. Since theoretically unsupported data of higher dimensionality and higher complexity would benefit most from the power of explicit magnification control, we conduct systematic simulations on “forbidden ” data. For the unsupported =2 cases that we investigate, the simulations show that even n though the magnification exponent achieved achieved by magnification control is not the same as the desired desired, achieved systematically follows desired with a slowly increasing positive offset. We show that for simple synthetic higher dimensional data information, theoretically optimum pdf matching ( achieved =1) can be achieved, and that negative magnification has the desired effect of improving the detectability of rare classes. In addition, we further study theoretically unsupported cases with real data. Index Terms—Data mining, highdimensional data, map magnification, selforganizing maps (SOMs).
Learning Nonlinear Principal Manifolds by SelfOrganising Maps
"... This chapter provides an overview on the selforganised map (SOM) in the context of manifold mapping. It first reviews the background of the SOM and issues on its cost function and topology measures. Then its variant, the visualisation induced SOM (ViSOM) proposed for preserving local metric on the ..."
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Cited by 4 (0 self)
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This chapter provides an overview on the selforganised map (SOM) in the context of manifold mapping. It first reviews the background of the SOM and issues on its cost function and topology measures. Then its variant, the visualisation induced SOM (ViSOM) proposed for preserving local metric on the map, is introduced and reviewed for data visualisation. The relationships among the SOM, ViSOM, multidimensional scaling, and principal curves are analysed and discussed. Both the SOM and ViSOM produce a scaling and dimensionreduction mapping or manifold of the input space. The SOM is shown to be a qualitative scaling method, while the ViSOM is a metric scaling and approximates a discrete principal curve/surface. Examples and applications of extracting data manifolds using SOMbased techniques are presented.
The SelfOrganizing Maps: Background, Theories, Extensions and Applications
"... For many years, artificial neural networks (ANNs) have been studied and used to model information processing systems based on or inspired by biological neural structures. They not only can provide solutions with improved performance when compared with traditional problemsolving methods, but ..."
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Cited by 1 (0 self)
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For many years, artificial neural networks (ANNs) have been studied and used to model information processing systems based on or inspired by biological neural structures. They not only can provide solutions with improved performance when compared with traditional problemsolving methods, but
Self Organizing Map algorithm and distortion measure
, 2008
"... 1 Self Organizing Map algorithm and distortion measure We study the statistical meaning of the minimization of distortion measure and the relation between the equilibrium points of the SOM algorithm and the minima of distortion measure. If we assume that the observations and the map lie in an compac ..."
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1 Self Organizing Map algorithm and distortion measure We study the statistical meaning of the minimization of distortion measure and the relation between the equilibrium points of the SOM algorithm and the minima of distortion measure. If we assume that the observations and the map lie in an compact Euclidean space, we prove the strong consistency of the map which almost minimizes the empirical distortion. Moreover, after calculating the derivatives of the theoretical distortion measure, we show that the points minimizing this measure and the equilibria of the Kohonen map do not match in general. We illustrate, with a simple example, how this occurs.
MAGNIFICATION CONTROL
"... We examine the scope of validity of the explicit SOM magnification control scheme of Bauer, Der, and Herrmann [1], on data for which the theory does not guarantee success, namely data that are ndimensional, n ≥ 2 and whose components in the different dimensions are not statistically independent. Th ..."
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We examine the scope of validity of the explicit SOM magnification control scheme of Bauer, Der, and Herrmann [1], on data for which the theory does not guarantee success, namely data that are ndimensional, n ≥ 2 and whose components in the different dimensions are not statistically independent. The Bauer et al. algorithm is very attractive for the possibility of faithful representation of the pdf of a data manifold, or for discovery of rare events, among other properties. Since theoretically unsupported data of higher dimensionality and higher complexity would benefit most from the power of explicit magnification control, we conduct systematic simulations on “forbidden ” data. For the unsupported n = 2 cases that we investigate the simulations show that even though the magnification exponent αachieved achieved by magnification control is not the same as the desired αdesired, αachieved systematically follows αdesired with a slowly increasing positive offset. We show that for simple synthetic higherdimensional data information theoretically optimum pdf matching (α achieved = 1) can be achieved, and that negative magnification has the desired effect of improving the detectability of rare classes. In addition we further study theoretically unsupported cases with real data.