Results 1  10
of
18
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 ..."
Abstract

Cited by 247 (14 self)
 Add to MetaCart
(Show Context)
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 ..."
Abstract

Cited by 246 (1 self)
 Add to MetaCart
(Show Context)
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 ..."
Abstract

Cited by 23 (13 self)
 Add to MetaCart
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.
Explicit magnification control of selforganizing maps for ‘forbidden’ data
"... 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 ..."
Abstract

Cited by 14 (7 self)
 Add to MetaCart
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.
Magnification control in selforganizing maps and neural gas
 NEURAL COMPUTATION
, 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 ..."
Abstract

Cited by 11 (5 self)
 Add to MetaCart
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.
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 ..."
Abstract

Cited by 10 (4 self)
 Add to MetaCart
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.
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 ..."
Abstract

Cited by 6 (0 self)
 Add to MetaCart
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 ..."
Abstract

Cited by 6 (0 self)
 Add to MetaCart
(Show Context)
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
Curr. Issues Mol. Biol. (2002) 4: 4556. An Overview of Toxicogenomics
"... Toxicogenomics is a rapidly developing discipline that promises to aid scientists in understanding the molecular and cellular effects of chemicals in biological systems. This field encompasses global assessment of biological effects using technologies such as DNA microarrays or high throughput NMR a ..."
Abstract
 Add to MetaCart
(Show Context)
Toxicogenomics is a rapidly developing discipline that promises to aid scientists in understanding the molecular and cellular effects of chemicals in biological systems. This field encompasses global assessment of biological effects using technologies such as DNA microarrays or high throughput NMR and protein expression analysis. This review provides an overview of advancing multiple approaches (genomic, proteomic, metabonomic) that may extend our understanding of toxicology and highlights the importance of coupling such approaches with classical toxicity studies. Background and Definition of Toxicogenomics The field of toxicology is defined as the study of stressors and their adverse effects. One subdiscipline deals with hazard identification, mechanistic toxicology, and risk assessment. Increased understanding of the mechanism of action of chemicals being assayed will improve the efficiency of these tasks. However, the derivation of mechanistic knowledge traditionally evolves from studying a few genes at a time in order to implicate their function in mediation of toxicant effects. Undoubtedly, this process has to be accelerated to monitor and discern the effects of the thousands of new compounds developed by the chemical and pharmaceutical industries. There is a need for a screening method that can offer some insight into the potential adverse outcome(s) of new drugs allowing the intelligent advancement of compounds into late stages of safety evaluation. The rapid development and evolution of genomic
Publication 7 Clustering of the Self−Organizing Map
"... Abstract—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 facilit ..."
Abstract
 Add to MetaCart
(Show Context)
Abstract—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 procedure—first using SOM to produce the prototypes that are then clustered in the second stage—is found to perform well when compared with direct clustering of the data and to reduce the computation time. Index Terms—Clustering, data mining, exploratory data analysis, selforganizing map. I.