1 VISUALIZATION OF GEOSPATIAL DATA BY COMPONENT PLANES AND U-MATRIX Marcos Aurélio Santos da Silva 1, Antônio Miguel Vieira Monteiro 2 and José Simeão Medeiros 2 1 Embrapa Tabuleiros Costeiros - Laboratory of Applied Geothecnologies Av. Beira Mar, 3250, , Aracaju, SE, Brazil.; 2 National Institute for Space Research - INPE, Caixa Postal 515, , São José dos Campos, SP, Brazil. Abstract: Key words: This paper shows an application of two visualization algorithms of multivariate data, U-matrix and Component Planes, in a matter of exploratory analysis of geospatial data. These algorithms were applied in the investigation of urban social exclusion/inclusion in the city of São José dos Campos - SP, Brasil. Self-organizing Map; multivariate analysis; spatial analysis; visualization. 1. INTRODUCTION Modern data acquisition techniques are offering tremendous opportunities that result in more geospatial data to be handled. Analyzing these data becomes a difficult task due to their complexity and hidden patterns. The complexity of the attribute space in such complex datasets does not always allows traditional deductive and statistically based approaches to analyse that data. Like many other techniques, Artificial Neural Network (ANN) is an emerging solution for pattern recognition. Among the ANN models, Self-Organizing Maps (SOM) is seen as a good technique for exploratory analysis of data (Kohonen, 2001). In this paper we explored a SOM, an unsupervised ANN, and their visualization algorithms, U-matrix (Ultsch, 1993) and Component Planes (Kohonen, 2001), for an exploratory analysis of geospatial data. These algorithms are visualization tools developed to work closer to SOM algorithm.
2 76 SOM has being applied successfully in a variety of problems of exploratory analysis of multivariated data (Kohonen, 2001), however, few are the works related to the analysis of geospatial data (Winter and Hewitson, 1994; Open-shaw and Turton, 1996; Babu, 1997; Kaski and Kohonen, 1996; Foody, 1999; Cereghino et al., 2001; Park et al., 2003). Urban geospatial problem is also a unexplored theme (Franzini et al., 2001). The main goal here is to find out how the dataset is distributed, how each variable correlates with each other and if there is some spatial correlation between the feature and physical spaces in a exploratory manner of urban geospatial data (Bailey and Gatrell, 1995). Section 2 explains about the SOM and visualization related algorithms, U-matrix and Component Planes. Section 3 presents our case study, that is mapping urban social exclusion/inclusion in the city of São José dos Campos, São Paulo, Brazil. Finally, Section 4 shows our results and discussion and Section 5 our conclusions. 2. SELF-ORGANIZING MAPS Kohonen Self-Organizing Map is a competitive artificial neural network structured in two layers (Kohonen, 2001), see Fig. 1. The first one represents the input data, x k, the second one is a neuron s grid, usually bidimensional, full connected. Each neuron has one codevector associated, w j. The main goal of the SOM algorithm is to approximate the input dataset preserving structural local proximities to statistical properties among them. Therefore, it means that SOM acts as a data compressor and feature extractor. A learning process achieves this.
3 77 Figure 1. This picture illustrates an architecture of a two dimensional (NxM) SOM with a x k input vector and w j codevector. The learning process can be split into three phases. In the first phase, competitive, each input pattern is presented to all neurons searching for the Best Match Unit (BMU) using Euclidean distance measure. In the second phase, cooperative, a neighborhood relation, among the BMU and the other neurons, is defined by a neighborhood kernel function (h ij ). Finally, in the last phase, adaptive, BMU and neighbors codevectors will be updated using some kind of adaptive rule, see Eqs.(1)-(2) (Vesanto, 1999). n V = i i t x j j s ( ) (1) m hji ( t) s j ( t) j wi ( t + 1) = (2) m n h ( t) j V j ji where: s i represents an input pattern sum for the ith-voronoi region, V i ; and nv i is the number of samples for the Voronoi dataset of the ith-neuron. h(t) is the neighborhood kernel function a t time; m is the number of Voronoi regions. After the learning process codevectors should approximate, in a non-linear manner, the input data. Besides, SOM preserves the topological structure of
4 78 input data, so nearby patterns in the sample dataset are associated with nearby neurons in the SOM grid. SOM can vary as learning algorithm, grid's topological structure, neighborhood kernel function, initial parameterization etc. For this work we have choose a SOM batch learning because it generates the same result for the same initial parameters and there isn't learning rate (Kohonen, 2001). We defined and tested a limited set of SOM's configurations varying initial radius of neighborhood kernel function and the size of grid. All networks were bidimensional, has hexagonal grid, gaussian neighborhood kernel function, linear initialization, batch learning and only one learning phase. We used topological and quantization errors as quality metrics. Visual quality analysis was also used. To proceed our exploratory analysis we used two software, SOM Toolbox and CASAA. SOM Toolbox is a Matlab based package that implements all algorithms needed here, but we only used it to show visual results (Vesanto et al., 1999). Connectionist Approach for Spatial Analysis of Area (CASAA) is a software for SOM simulation created to manipulate geospatial data stored in a TerraLib database format (Câmara et al., 2002). Figure 2 shows the main screen of that system. Figure 2. The main screen of the CASAA system.
5 2.1 U-matrix 79 The Unified distance matrix (U-matrix) makes the 2D visualization of multi-variate data possible using SOM's codevectors as data source. This is achieved by using topological relations property among neurons after the learning process. This algorithm generates a matrix where each component is a distance measure between two adjacent neurons, therefore we can visualize any multi-variated dataset in a two-dimensional display. Figure 3 shows an representation of an U-matrix calculation for an 3x3 2D hexagonal SOM. By U-matrix we can detect topological relations among neurons and infer about the input data structure. High values in the U-matrix represent a frontier region between clusters, and low values represent a high degree of similarities among neurons on that region, clusters. This can be a visual task when we use some color schema. Nevertheless, this visual interpretation can be very hard for very short U- matrices because short SOM generates complex U-matrix when we are treating real datasets. Figure 3. U-matrix generation example for an 3x3 hexagonal SOM. 2.2 Component Planes (CP) After the learning process we can color each neuron according with each component value in the codevector. Therefore, we will take a colored SOM for each variable (Fig. 4). Trought these Component Planes we can realize emerging patterns of data distribution on SOM's grid (Kohonen, 2001), detect correlations among variables and the contribution of each one to the SOM differentiation only viewing the colored pattern for each Component Plane.
6 80 Figure 4. One Component Plane example for an 3x3 hexagonal SOM. Some works used U-matrix and CP for spatial analysis, Kaski & Kohonen (1996) applied both to classify countries by their socioeconomic global indexes. Winter & Hewitson (1994) used CP to analyze the racial segregation matter in a City South Africa. Franzini et al. (2001) used a short SOM for clustering of urban spatial regions using socioeconomic data. These applications are different from our approach for some reasons: a) we used a batch SOM learning algorithm, they used an online learning scheme; b) they did not work with intra-urban dataset or high resolution, they work with global values that means low resolution; c) they did not use any automatic segmentation of data using CP, here we proposed an simple but very efficient procedure to do this. 3. CASE STUDY: MAPPING URBAN SOCIAL EXCLUSION/INCLUSION Our case study is the mapping urban social exclusion/inclusion in the City of São José dos Campos, São Paulo, through composite indexes created by Genovez (2002) using the Brazilian Census Bureau (IBGE) data for each urban census region (Fig. 5). The main goal here is to find out how the dataset is distributed, how each variable correlates with each other and if there is some spatial correlation between the feature and physical spaces (Bailey and Gatrell, 1995).
7 81 Figure 5. Urban census sectors for São José dos Campos, SP. For this finding we used 8 socioeconomic indexes. Each one varying from -1, high level of social exclusion, to +1, high level of social inclusion. They are: familiar income (IFH), educational development (ED), educational stimulus (ES), longevity (LONG), environmental quality (EQ), home quality (PQ), concentration of family headed by women (CIWFH) and concentration of family headed by illiterate women (CWFH). 4. RESULTS AND DISCUSSION Looking at the topological and quantization error graphics (Fig. 6) we realized that the quantization curve declines smoothly for an y asymptotic, so how big is the SOM how bit will be the quantization error, that means that more neurons represents better input pattern. In the topological graphic we can see an random behavior after 15th SOM's network configuration, so we cannot make any strong conclusion, only that for very short SOM we will have high values for the topological metric. These results were also reached by others authors (Kohonen, 2001). Therefore, we avoid short SOMs for U-matrix and Component Planes analysis.
8 82 Figure 6. Error graphics for topologial and quantization error metrics. 4.1 General structure of dataset and outliers A well formation of an U-matrix depends of the quality convergence learning, that depends of the dataset structure, SOM grid size and the initial radius of the neighborhood kernel function. Figure 7 shows that with a short U-matrix, 9x9, we cannot see significant differences thought colored patterns because there is too few neurons to absolve the complexity of dataset. The same figure shows also that to a big U-matrix, 99x59, happens the opposite, there are too many neurons to map the data complexity, so we can see many short clusters, that represents an overtrained neural network. This visual analysis confirms the graphical analysis of the quality metric errors and shows to us that very big neural networks does not fit well, considering our case. Consequently, we've chose an intermediate 20x15 SOM to proceed our analysis.
9 83 Figure 7. These figures show that for our case neither short nor big SOM does not fits well. Figure 8. U-matrix for an 20x15 hexagonal SOM grid.
10 84 Figure 8 shows an U-matrix for an 20x15 SOM network. Looking at that picture we identify three patterns. First, there are two short clusters areas on the two top corners of picture. This suggests that these areas could be associated with census sectors outliers. Second, there is a big homogeneous area on the middle of the U-matrix. Here we can make two inferences, or the dataset has a high degree of similarity or the U-matrix could not separate the dataset in a proper manner. Finally, on the bottom of the U-matrix we register some differentiation, but not clear enough to be a cluster. Mapping census areas associated to neurons located on the two top corners of U-matrix we identify, visually, that we have a set of census sectors outliers, Fig. 9. In fact, these areas have differences when compared with the others. Some of them has high exclusion indexes values but is located in a high inclusion area. Another ones is located in a high exclusion area but has high inclusion values for some indexes. Figure 9. Census sectors highlighted represents outliers detected by the U-matrix visualization. 4.2 Component analysis and Spatial distribution of phenomena If the U-matrix vary, significantly, for different size and neighbor radius the CP presents an opposite behavior. Although short SOM hide something
11 from us, in general, all CP shows a colored pattern that represents how variables is distributed in the SOM grid, Figure 10 shows two CPs for a short and a big CP SOM grid. Therefore, we've chose the same 20x15 hexagonal SOM to proceed our component analysis. Figure 11 shows an U-matrix and eight CP, one for each studied variable. High values is associated with red color and low values with blue colors. High values, near +1, also mean high level of urban social inclusion, and low values, near -1, mean high degree of urban social exclusion. Looking at Component Planes we can identify some visible patterns among components. First, IFH and ED have a very similar color pattern, this suggests that they should be strongly correlated. Second, LONG and EQ presents a very homogeneous colored pattern, so these variables could have a low contribution for differentiation among dataset. 85 Figure 10. These figures shows that we can find the same color pattern for short or big Self- Organizing Maps. Component Planes presents the same color pattern distribution for all CP, every one has high values on bottom and low values on top, with some exceptions. Therefore, we have a social exclusion-inclusion direction in SOM grid, and it is vertical (Fig. 12). Labeling all neurons starting on top to the bottom (vertical direction) and mapping this labeling to the map of census sectors we will generate a colored map illustrated by Fig. 13a.
12 86 Analyzing that map we concluded that the most inclusion's sectors, with high values, are located in center of the map, and the most of exclusion's sectors are located on peripherical areas. So, there are a general direction of the urban social exclusion/inclusion spatial distribution on sectors map, and it is center-to-peripherical zone. This result was also achieved using multivariated statistics (Genovez, 2002), see Fig. 13b. Figure 11. Component Planes for each variable using an 20x15 hexagonal SOM.
13 87 Figure 12. Possible directions of data distribution in SOM's grid. Figure 13. Figure 'a' shows the social inclusion/exclusion map segmentation using neural approach (SOM). Figure 'b' shows the same segmentation using statistical techniques, Iex created by Genovez (2002). 5. CONCLUSIONS This experiment showed that SOM and related visualization algorithms could be applied as an exploratory tool to investigate an urban matter with good results. In our case we also confirmed previous statistical results reached by Genovez (2002). The quality of U-matrix and CP can be viewed easily and can be measured by topological and quantization errors with some
14 88 cautions. CP was also used as spatial analysis tool, mapping the direction distribution on SOM grid onto the census sectors map. Initial radius of the neighborhood kernel function and the size of the SOM grid affect the final quality of the neural network and, consequently, the U-matrix and CP. Nevertheless, the size influences more than the initial radius. Short and big SOMs weren't good for visual interpretations. The automatic segmentation of census sectors based on CP patterns helped us to see the spatial distribution of the phenomena and suggests that there are a strong spatial dependence between feature and physical spaces. Although, more studies must be carried out to evaluate these visualization techniques for other spatial pattern problems, there is not any restriction for U-matrix and CP applications to other kind of spatial problems. 6. REFERENCES Babu, G. P. (1997). Self-organizing neural networks for spatial data. Pattern Recognition Letters, 18: Bailey, T. C. and Gatrell, A. C. (1995). Interactive Spatial Data Analysis. Longman. Cereghino, R., Giraudel, J., and Compin, A. (2001). Spatial analysis of stream invertebrates distribution in the adour-garonne drainage basin (france), using kohonen self organizing maps. Ecological Modelling, 146(1-3): Câmara, G., Neves, M., Monteiro, A., Souza, R., Paiva, J. A., and Vinhas, L. (2002). Spring and terralib: Integrating spatial analysis and gis. In for Spatially Integrated Social Science, C., editor, Specialist Meeting on Spatial Data Analysis Software Tools, Santa Barbara, CA. Foody, G. (1999). Applications of the self-organising feature map neural network in community data analysis. Ecological Modelling, 120: Franzini, L., Bolchi, P., and Diappi, L. (2001). Self Organizing Maps,: A Clustering neural method for urban analysis. In Banos, A., Banos, F., Bolot, J., and Coutherut, C., editors, Proceeding of the VRecontres de Théo Quant., pages Genovez, P. C. (2002). Território e desigualdades: Análise espacial intra-urbana no estudo da dinâmica de exclusão/inclusão social no espaço urbano em são josé dos campos-sp. Master's thesis, INPE. Kaski, S. and Kohonen, T. (1996). Exploratory Data Analysis by The Self-Organizing Map: Structures of Welfare and Poverty in the World. In Refenes, A.-P. N., Abu-Mostafa, Y., Moody, J., and Weigend, A., editors, Proceeding of the Third International Conference on Neural Networks in the Capital Markets, pages World Scientific. Kohonen, T. (2001). Self-organizing maps. Springer. Third Edition. Openshaw, S. and Turton, I. (1996). A parallel kohonen algorithm for the classification of large spatial datasets. Computers & Geosciences, 22(9): Park, Y.-S., Cereghino, R., Compin, A., and Lek, S. (2003). Applications of artificial neural networks for patterning and predicting aquatic insect species richness in running waters. Ecological Modelling, 160: Ultsch, A. (1993). Knowledge extraction from self-organizing neural networks. In Opitz, O., editor, Information and Classification. Springer. Vesanto, J. (1999). Som based data visualization methods. Intelligent Data Analysis, 3:
15 Vesanto, J., Himberg, J., Alhoniemi, E., and Parhankangas, J. (1999). Self-Organizing Map in Matlab: the SOM Toolbox. In Proceeding of the Matlab DSP Conference, pages 35^10. Winter, K. and Hewitson, B. (1994). Self organizing maps - applications to census data. In Hewitson, B. and Crane, R., editors, Neural nets: applications in geography. Kluwer. 89
International Journal of Science and Technology Volume 3 No. 2, February, 2014 Visualization of Breast Cancer Data by SOM Component Planes P.Venkatesan. 1, M.Mullai 2 1 Department of Statistics,NIRT(Indian
On the use of Three-dimensional Self-Organizing Maps for Visualizing Clusters in Geo-referenced Data Jorge M. L. Gorricha and Victor J. A. S. Lobo CINAV-Naval Research Center, Portuguese Naval Academy,
USING SELF-ORGANIZING MAPS FOR INFORMATION VISUALIZATION AND KNOWLEDGE DISCOVERY IN COMPLEX GEOSPATIAL DATASETS Koua, E.L. International Institute for Geo-Information Science and Earth Observation (ITC).
Visualization of Crime Trajectories with Self-Organizing Maps: A Case Study on Evaluating the Impact of Hurricanes on Spatio- Temporal Crime Hotspots Abstract Julian Hagenauer 1, Marco Helbich 1, Michael
A Discussion on Visual Interactive Data Exploration using Self-Organizing Maps Julia Moehrmann 1, Andre Burkovski 1, Evgeny Baranovskiy 2, Geoffrey-Alexeij Heinze 2, Andrej Rapoport 2, and Gunther Heidemann
Icon and Geometric Data Visualization with a Self-Organizing Map Grid Alessandra Marli M. Morais 1, Marcos Gonçalves Quiles 2, and Rafael D. C. Santos 1 1 National Institute for Space Research Av dos Astronautas.
Visualization of large data sets using MDS combined with LVQ. Antoine Naud and Włodzisław Duch Department of Informatics, Nicholas Copernicus University, Grudziądzka 5, 87-100 Toruń, Poland. www.phys.uni.torun.pl/kmk
Monitoring of Complex Industrial Processes based on Self-Organizing Maps and Watershed Transformations Christian W. Frey 2012 Monitoring of Complex Industrial Processes based on Self-Organizing Maps and
IEEE TRANSACTIONS ON NEURAL NETWORKS, VOL. 13, NO. 1, JANUARY 2002 237 ViSOM A Novel Method for Multivariate Data Projection and Structure Visualization Hujun Yin Abstract When used for visualization of
Comparing large datasets structures through unsupervised learning Guénaël Cabanes and Younès Bennani LIPN-CNRS, UMR 7030, Université de Paris 13 99, Avenue J-B. Clément, 93430 Villetaneuse, France email@example.com
ENERGY RESEARCH at the University of Oulu 129 Use of self-organizing maps in studying ordinary air emission measurements of a power plant Kimmo Leppäkoski* and Laura Lohiniva University of Oulu, Department
Technical Report OeFAI-TR-2002-29, extended version published in Proceedings of the International Conference on Artificial Neural Networks, Springer Lecture Notes in Computer Science, Madrid, Spain, 2002.
Methodology for Emulating Self Organizing Maps for Visualization of Large Datasets Macario O. Cordel II and Arnulfo P. Azcarraga College of Computer Studies *Corresponding Author: firstname.lastname@example.org
Decision Support via Big Multidimensional Data Visualization Audronė Lupeikienė 1, Viktor Medvedev 1, Olga Kurasova 1, Albertas Čaplinskas 1, Gintautas Dzemyda 1 1 Vilnius University, Institute of Mathematics
Information Visualization with Self-Organizing Maps Jing Li Abstract: The Self-Organizing Map (SOM) is an unsupervised neural network algorithm that projects highdimensional data onto a two-dimensional
Visualising Class Distribution on Self-Organising Maps Rudolf Mayer, Taha Abdel Aziz, and Andreas Rauber Institute for Software Technology and Interactive Systems Vienna University of Technology Favoritenstrasse
USING SELF-ORGANISING MAPS FOR ANOMALOUS BEHAVIOUR DETECTION IN A COMPUTER FORENSIC INVESTIGATION B.K.L. Fei, J.H.P. Eloff, M.S. Olivier, H.M. Tillwick and H.S. Venter Information and Computer Security
Customer Data Mining and Visualization by Generative Topographic Mapping Methods Jinsan Yang and Byoung-Tak Zhang Artificial Intelligence Lab (SCAI) School of Computer Science and Engineering Seoul National
, July 4-6, 2012, London, U.K. Data Clustering and Topology Preservation Using 3D Visualization of Self Organizing Maps Z. Mohd Zin, M. Khalid, E. Mesbahi and R. Yusof Abstract The Self Organizing Maps
488 Nonlinear Analysis: Modelling and Control, 2011, Vol. 16, No. 4, 488 504 Visual analysis of self-organizing maps Pavel Stefanovič, Olga Kurasova Institute of Mathematics and Informatics, Vilnius University
INTERACTIVE DATA EXPLORATION USING MDS MAPPING Antoine Naud and Włodzisław Duch 1 Department of Computer Methods Nicolaus Copernicus University ul. Grudziadzka 5, 87-100 Toruń, Poland Abstract: Interactive
CITY UNIVERSITY OF HONG KONG 香 港 城 市 大 學 Self-Organizing Map: Visualization and Data Handling 自 組 織 神 經 網 絡 : 可 視 化 和 數 據 處 理 Submitted to Department of Electronic Engineering 電 子 工 程 學 系 in Partial Fulfillment
Data Mining and Neural Networks in Stata 2 nd Italian Stata Users Group Meeting Milano, 10 October 2005 Mario Lucchini e Maurizo Pisati Università di Milano-Bicocca email@example.com firstname.lastname@example.org
Self Organizing Maps: Fundamentals Introduction to Neural Networks : Lecture 16 John A. Bullinaria, 2004 1. What is a Self Organizing Map? 2. Topographic Maps 3. Setting up a Self Organizing Map 4. Kohonen
061215-064 1 A user friendly toolbox for exploratory data analysis of underwater sound Fernando J. Pires 1 and Victor Lobo 2, Member, IEEE Abstract The underwater acoustics research group at the Portuguese
Data Mining of Collaboratively Collected Geographic Crime Information Using an Unsupervised Neural Network Approach Julian Hagenauer, Marco Helbich (corresponding author), Michael Leitner, Jerry Ratcliffe,
A Computational Framework for Exploratory Data Analysis Axel Wismüller Depts. of Radiology and Biomedical Engineering, University of Rochester, New York 601 Elmwood Avenue, Rochester, NY 14642-8648, U.S.A.
Clustering census data: comparing the performance of self-organising maps and k-means algorithms Fernando Bação 1, Victor Lobo 1,2, Marco Painho 1 1 ISEGI/UNL, Campus de Campolide, 1070-312 LISBOA, Portugal
A Partially Supervised Metric Multidimensional Scaling Algorithm for Textual Data Visualization Ángela Blanco Universidad Pontificia de Salamanca email@example.com Spain Manuel Martín-Merino Universidad
Remote Sensing and Geoinformation Lena Halounová, Editor not only for Scientific Cooperation EARSeL, 2011 Multiscale Object-Based Classification of Satellite Images Merging Multispectral Information with
TW72 UNIVERSITY OF BOLTON SCHOOL OF ENGINEERING MS SYSTEMS ENGINEERING AND ENGINEERING MANAGEMENT SEMESTER 1 EXAMINATION 2015/2016 INTELLIGENT SYSTEMS MODULE NO: EEM7010 Date: Monday 11 th January 2016
Quality Assessment in Spatial Clustering of Data Mining Azimi, A. and M.R. Delavar Centre of Excellence in Geomatics Engineering and Disaster Management, Dept. of Surveying and Geomatics Engineering, Engineering
Visualization of Agriculture Data Using Self-Organizing Maps Georg Ruß, Rudolf Kruse, Martin Schneider, Peter Wagner Abstract The importance of carrying out effective and sustainable agriculture is getting
1 Forecasting Women s Apparel Sales Using Mathematical Modeling Celia Frank* 1, Balaji Vemulapalli 1, Les M. Sztandera 2, Amar Raheja 3 1 School of Textiles and Materials Technology 2 Computer Information
WSEAS Transactions on Systems Issue 3, Volume 2, July 2003, ISSN 1109-2777 629 Analysis of Performance Metrics from a Database Management System Using Kohonen s Self Organizing Maps Claudia L. Fernandez,
A General Tracking and Auditing Architecture for the OpenACS framework Jorge Couchet 1, Olga C. Santos 2, Emmanuelle Raffenne 2, Jesús G. Boticario 2, and Daniel Manrique 3 1 Applied Artificial Intelligence
IEEE TRANSACTIONS ON NEURAL NETWORKS, VOL. 20, NO. 4, APRIL 2009 549 Exploiting Data Topology in Visualization and Clustering of Self-Organizing Maps Kadim Taşdemir and Erzsébet Merényi, Senior Member,
Online data visualization using the neural gas network Pablo A. Estévez, Cristián J. Figueroa Department of Electrical Engineering, University of Chile, Casilla 412-3, Santiago, Chile Abstract A high-quality
A publication of CHEMICAL ENGINEERINGTRANSACTIONS VOL. 33, 2013 Guest Editors: Enrico Zio, Piero Baraldi Copyright 2013, AIDIC ServiziS.r.l., ISBN 978-88-95608-24-2; ISSN 1974-9791 The Italian Association
Volume 3, No. 8, August 2012 Journal of Global Research in Computer Science REVIEW ARTICLE Available Online at www.jgrcs.info Comparison of Supervised and Unsupervised Learning Classifiers for Travel Recommendations
Expanding Self-organizing Map for Data Visualization and Cluster Analysis Huidong Jin a,b, Wing-Ho Shum a, Kwong-Sak Leung a, Man-Leung Wong c a Department of Computer Science and Engineering, The Chinese
Neural Network Add-in Version 1.5 Software User s Guide Contents Overview... 2 Getting Started... 2 Working with Datasets... 2 Open a Dataset... 3 Save a Dataset... 3 Data Pre-processing... 3 Lagging...
Self Organizing Maps for Visualization of Categories Julian Szymański 1 and Włodzisław Duch 2,3 1 Department of Computer Systems Architecture, Gdańsk University of Technology, Poland, firstname.lastname@example.org
Local Anomaly Detection for Network System Log Monitoring Pekka Kumpulainen Kimmo Hätönen Tampere University of Technology Nokia Siemens Networks email@example.com firstname.lastname@example.org Abstract
Visualization of Topology Representing Networks Agnes Vathy-Fogarassy 1, Agnes Werner-Stark 1, Balazs Gal 1 and Janos Abonyi 2 1 University of Pannonia, Department of Mathematics and Computing, P.O.Box
6.2.8 Neural networks for data mining Walter Kosters 1 In many application areas neural networks are known to be valuable tools. This also holds for data mining. In this chapter we discuss the use of neural
QÜESTIIÓ, vol. 25, 3, p. 509-520, 2001 PRACTICAL DATA MINING IN A LARGE UTILITY COMPANY GEORGES HÉBRAIL We present in this paper the main applications of data mining techniques at Electricité de France,
Neural networks and their rules for classification in marine geology Alfred Ultsch 1, Dieter Korus 2, Achim Wehrmann 3 Abstract Artificial neural networks are more and more used for classification. They
Security Threats to Signal Classifiers Using Self-Organizing Maps T. Charles Clancy Awais Khawar email@example.com firstname.lastname@example.org Department of Electrical and Computer Engineering University of Maryland, College
Expert Systems with Applications 27 (2004) 27 33 www.elsevier.com/locate/eswa Segmentation of stock trading customers according to potential value H.W. Shin a, *, S.Y. Sohn b a Samsung Economy Research
International Journal of Civil & Environmental Engineering IJCEE-IJENS Vol:13 No:03 46 Classification of Engineering Consultancy Firms Using Self-Organizing Maps: A Scientific Approach Mansour N. Jadid
IMPROVED METHODS FOR CLUSTER IDENTIFICATION AND VISUALIZATION IN HIGH-DIMENSIONAL DATA USING SELF-ORGANIZING MAPS. A Thesis Presented by Narine Manukyan to The Faculty of the Graduate College of The University
Neural Networks Introduction to Artificial Intelligence CSE 150 May 29, 2007 Administration Last programming assignment has been posted! Final Exam: Tuesday, June 12, 11:30-2:30 Last Lecture Naïve Bayes
2005 IEEE International Symposium on Signal Processing and Information Technology Detecting Denial of Service Attacks Using Emergent Self-Organizing Maps Aikaterini Mitrokotsa, Christos Douligeris Department
Contents List of Figures Foreword Preface xxv xxiii xv Acknowledgments xxix Chapter 1 Fraud: Detection, Prevention, and Analytics! 1 Introduction 2 Fraud! 2 Fraud Detection and Prevention 10 Big Data for
COMPARISON OF OBJECT BASED AND PIXEL BASED CLASSIFICATION OF HIGH RESOLUTION SATELLITE IMAGES USING ARTIFICIAL NEURAL NETWORKS B.K. Mohan and S. N. Ladha Centre for Studies in Resources Engineering IIT
Bull. Nov. Comp. Center, Comp. Science, 25 (2006), 69 74 c 2006 NCC Publisher Load balancing in a heterogeneous computer system by self-organizing Kohonen network Mikhail S. Tarkov, Yakov S. Bezrukov Abstract.
From IP port numbers to ADSL customer segmentation F. Clérot France Télécom R&D Overview ADSL customer segmentation: why? how? Technical approach and synopsis Data pre-processing The many faces of a Kohonen
In Apostolos-Paul N. Refenes, Yaser Abu-Mostafa, John Moody, and Andreas Weigend (Eds.) Neural Networks in Financial Engineering. Proceedings of the Third International Conference on Neural Networks in
High-dimensional labeled data analysis with Gabriel graphs Michaël Aupetit CEA - DAM Département Analyse Surveillance Environnement BP 12-91680 - Bruyères-Le-Châtel, France Abstract. We propose the use
The Value of Visualization 2 G Janacek -0.69 1.11-3.1 4.0 GJJ () Visualization 1 / 21 Parallel coordinates Parallel coordinates is a common way of visualising high-dimensional geometry and analysing multivariate
The Result Analysis of the Cluster Methods by the Classification of Municipalities PAVEL PETR, KAŠPAROVÁ MILOSLAVA System Engineering and Informatics Institute Faculty of Economics and Administration University
Using Data Mining for Mobile Communication Clustering and Characterization A. Bascacov *, C. Cernazanu ** and M. Marcu ** * Lasting Software, Timisoara, Romania ** Politehnica University of Timisoara/Computer
Advanced Web Usage Mining Algorithm using Neural Network and Principal Component Analysis Arumugam, P. and Christy, V Department of Statistics, Manonmaniam Sundaranar University, Tirunelveli, Tamilnadu,
Predict Influencers in the Social Network Ruishan Liu, Yang Zhao and Liuyu Zhou Email: rliu2, yzhao2, email@example.com Department of Electrical Engineering, Stanford University Abstract Given two persons
TÍTULO Building clusters for CRM strategies by Subtítulo mining Building airlines clusters customer for CRM data strategies by mining airlines customer data Nome completo do Candidato Helena Sofia Guerreiro
CMPE 59H Comparison of Non-linear Dimensionality Reduction Techniques for Classification with Gene Expression Microarray Data Term Project Report Fatma Güney, Kübra Kalkan 1/15/2013 Keywords: Non-linear
Machine Learning with MATLAB David Willingham Application Engineer 2014 The MathWorks, Inc. 1 Goals Overview of machine learning Machine learning models & techniques available in MATLAB Streamlining the
EXPLORATORY GEOSPATIAL DATA ANALYSIS USING SELF-ORGANIZING MAPS Case Study of Portuguese Mainland Regions Fernando C. LOURENÇO, Victor S. LOBO and Fernando L. BAÇÃO This paper describes the application
Method of Combining the Degrees of Similarity in Handwritten Signature Authentication Using Neural Networks Ph. D. Student, Eng. Eusebiu Marcu Abstract This paper introduces a new method of combining the
EXPERIMENTAL APPLICATIONS OF ARTIFICIAL NEURAL NETWORKS IN ENGINEERING PROCESSING SYSTEM S. Dadvandipour Institute of Information Engineering, University of Miskolc, Egyetemváros, 3515, Miskolc, Hungary,
Training a Self-Organizing Map distributed on a PVM network Nuno Bandeira Dep.Informatics, New University of Lisbon, Quinta da Torre 85 MONTE DA CAPARICA, PORTUGAL firstname.lastname@example.org Victor Jose Lobo Fernando
Available online at www.sciencedirect.com Energy Conversion and Management 49 (2008) 564 569 www.elsevier.com/locate/enconman Dynamic intelligent cleaning model of dirty electric load data Zhang Xiaoxing
A Review of Data Clustering Approaches Vaishali Aggarwal, Anil Kumar Ahlawat, B.N Panday Abstract- Fast retrieval of the relevant information from the databases has always been a significant issue. Different
9. Text & Documents Visualizing and Searching Documents Dr. Thorsten Büring, 20. Dezember 2007, Vorlesung Wintersemester 2007/08 Slide 1 / 37 Outline Characteristics of text data Detecting patterns SeeSoft
A Survey of Kernel Clustering Methods Maurizio Filippone, Francesco Camastra, Francesco Masulli and Stefano Rovetta Presented by: Kedar Grama Outline Unsupervised Learning and Clustering Types of clustering
EVALUATION OF NEURAL NETWORK BASED CLASSIFICATION SYSTEMS FOR CLINICAL CANCER DATA CLASSIFICATION K. Mumtaz Vivekanandha Institute of Information and Management Studies, Tiruchengode, India S.A.Sheriff
DEVELOPMENT OF AN INTEGRATED IMAGE PROCESSING AND GIS SOFTWARE FOR THE REMOTE SENSING COMMUNITY UBIRAJARA MOURA DE FREITAS 1, ANTÔNIO MIGUEL MONTEIRO 1, GILBERTO CÂMARA 1, RICARDO CARTAXO MODESTO SOUZA
Relative Permeability Measurement in Rock Fractures Siqi Cheng, Han Wang, Da Huo Abstract The petroleum industry always requires precise measurement of relative permeability. When it comes to the fractures,
HDDVis: An Interactive Tool for High Dimensional Data Visualization Mingyue Tan Department of Computer Science University of British Columbia email@example.com ABSTRACT Current high dimensional data visualization
Is a Data Scientist the New Quant? Stuart Kozola MathWorks 2015 The MathWorks, Inc. 1 Facts or information used usually to calculate, analyze, or plan something Information that is produced or stored by
Introduction to Neural Networks : Revision Lectures John A. Bullinaria, 2004 1. Module Aims and Learning Outcomes 2. Biological and Artificial Neural Networks 3. Training Methods for Multi Layer Perceptrons