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Projection of undirected and non-positional graphs using Self Organizing Maps

Markus Hagenbuchner, Shujia Zhang, Ah Chung Tsoi, Alessandro Sperduti
2009 The European Symposium on Artificial Neural Networks  
Models of Self-Organizing Maps for the treatment of graphs have also been defined and studied.  ...  Kohonen's Self-Organizing Map is a popular method which allows the projection of high dimensional data onto a low dimensional display space.  ...  Self-Organizing Maps for Graphs In general, SOMs [1] consist of a collection of neurons which are arranged on a regular q-dimensional grid called a display space or map, where often q=2.  ... 
dblp:conf/esann/HagenbuchnerZTS09 fatcat:jrvu53cdx5fknazr4dcitrrbqu

Self-Organizing Maps for Learning the Edit Costs in Graph Matching

M. Neuhaus, H. Bunke
2005 IEEE Transactions on Systems Man and Cybernetics Part B (Cybernetics)  
We propose a system of self-organizing maps that represent the distance measuring spaces of node and edge labels. Our learning process is based on the concept of self-organization.  ...  In the present paper we address the issue of learning graph edit distance cost functions for numerically labeled graphs from a corpus of sample graphs.  ...  The authors are with the Department of Computer Science, University of Bern, CH-3012 Bern, Switzerland (e-mail: mneuhaus@iam.unibe.ch, bunke@iam.unibe.ch).  ... 
doi:10.1109/tsmcb.2005.846635 pmid:15971918 fatcat:xduq2cmwendinannytdfaxwsf4

Sparsity Issues in Self-Organizing-Maps for Structures

Markus Hagenbuchner, Giovanni Da San Martino, Ah Chung Tsoi, Alessandro Sperduti
2011 The European Symposium on Artificial Neural Networks  
Recent developments with Self-Organizing Maps (SOMs) produced methods capable of clustering graph structured data onto a fixed dimensional display space.  ...  This paper discusses a limitation of the most powerful version of these SOMs, known as probability measure graph SOMs (PMGraphSOMs), viz., the sparsity induced by processing a large number of small graphs  ...  Self-Organizing Maps for Sparse Graphs In general, the more complex the learning problem is, the larger the map needs to be, and the dimension of the state vector grows with the size of the map.  ... 
dblp:conf/esann/HagenbuchnerMTS11 fatcat:vy4hyblinvab5i4onyiuptulnq

Context-Aware Visual Exploration of Molecular Datab

Giuseppe Fatta, Antonino Fiannaca, Riccardo Rizzo, Alfonso Urso, Michael Berthold, Salvatore Gaglio
2006 Sixth IEEE International Conference on Data Mining - Workshops (ICDMW'06)  
In this paper we describe an approach that allows for visual inspection of large collections of molecular compounds.  ...  In contrast to classical visualizations of such spaces we incorporate a specific focus of analysis, for example the outcome of a biological experiment such as high throughout screening results.  ...  Self-Organizing Maps for Clustering and Visualization Self-Organizing Maps [20] are neural networks inspired by some structure in biological brain.  ... 
doi:10.1109/icdmw.2006.51 dblp:conf/icdm/FattaFRUBG06 fatcat:sznn5tyvjnbjnpicdxczaglibe

Visual analysis of graphs with multiple connected components

Tatiana von Landesberger, Melanie Gorner, Tobias Schreck
2009 2009 IEEE Symposium on Visual Analytics Science and Technology  
Specifically, we use the Self-Organizing-Map algorithm in conjunction with a user-adaptable combination of graph features for clustering of graphs.  ...  The visualization of large graphs has recently received much research attention. However, specific systems for visual analysis of graph data sets consisting of many such components are rare.  ...  ACKNOWLEDGEMENTS This work was partially supported by the German Research Foundation (DFG) within the project Visual Feature Space Analysis as part of the Priority Program Scalable Visual Analytics (SPP  ... 
doi:10.1109/vast.2009.5333893 dblp:conf/ieeevast/LandesbergerGS09 fatcat:46dnplgr45d4bcxgkv5xd64vni

A self-organizing map for adaptive processing of structured data

M. Hagenbuchner, A. Sperduti, Ah Chung Tsoi
2003 IEEE Transactions on Neural Networks  
Here, we propose the first fully unsupervised model, namely an extension of traditional self-organizing maps (SOMs), for the processing of labeled directed acyclic graphs (DAGs).  ...  networks, recursive neural networks, self organizing maps (SOMs), vector quantization (VQ).  ...  The best known works include the temporal Kohonen map (TKM) [25] , and the recurrent self-organizing map (RSOM) [26] .  ... 
doi:10.1109/tnn.2003.810735 pmid:18238034 fatcat:77rczdvvcncfxiborheobzoceq

Optimal Clustering Algorithms for Data Mining

Omar Y. Alshamesti, Ismail M. Romi
2013 International Journal of Information Engineering and Electronic Business  
As a reason of the dependence of many applications on clustering techniques, while there is no combined method for clustering; this study compares kmean, Fu zzy c-mean, self-organizing map (SOM ), and  ...  Data mining is the process used to analyze a large quantity of heterogeneous data to extract useful informat ion.  ...  Self organizing map algoritms(SOM): Self-organizing map was proposed by Chokemen in 1982. It is a powerful method for clustering high dimensional data [7] .  ... 
doi:10.5815/ijieeb.2013.02.04 fatcat:ji6fqzz5ufastptc3y5pl4vowq

On-line relational SOM for dissimilarity data [article]

Madalina Olteanu, Marie Cottrell
2012 arXiv   pre-print
Several variants of the Self Organizing Map algorithm were introduced to generalize the original algorithm to this framework.  ...  The algorithm is tested on several datasets, including categorical data and graphs, and compared with the batch version and with other SOM algorithms for non vector data.  ...  In particular, several extensions of the Self-Organizing Maps (SOM) algorithm have been proposed.  ... 
arXiv:1212.6316v1 fatcat:nc3gpkguz5cpth75wb6iw7z6vm

On-Line Relational SOM for Dissimilarity Data [chapter]

Madalina Olteanu, Nathalie Villa-Vialaneix, Marie Cottrell
2013 Advances in Intelligent Systems and Computing  
Several variants of the Self Organizing Map algorithm were introduced to generalize the original algorithm to this framework.  ...  The algorithm is tested on several datasets, including categorical data and graphs, and compared with the batch version and with other SOM algorithms for non vector data.  ...  In particular, several extensions of the Self-Organizing Maps (SOM) algorithm have been proposed.  ... 
doi:10.1007/978-3-642-35230-0_2 fatcat:4smu6w566ffhdaojmyrlgnezom

Recurrent Self-Organizing Map for Severe Weather Patterns Recognition [chapter]

Jos Alberto, Brgida Rocha, Arthur Almeida, Jos Ricardo
2012 Recurrent Neural Networks and Soft Computing  
Results This section presents the results of the assessment among the studied networks: Self-Organizing Map (SOM), Temporal Kohonen Map (TKM) and Recurrent Self-Organizing Map (RSOM) for the severe weather  ...  Thus, this work analyzed the capacity of the Self-Organizing Map (SOM) and two of its temporal extensions: Temporal Kohonen Map and Recurrent Self-Organizing Map (Chappell & Taylor, 1993; Koskela et al  ... 
doi:10.5772/38823 fatcat:lpu2bfjp5zcqhgwlvsnwv6baey

Performance Evaluation of Basic Segmented Algorithms for Brain Tumor Detection

Suchita Yadav Suchita Yadav
2013 IOSR Journal of Electronics and Communication Engineering  
In this paper by experimental analysis and performance parameters the segmentation of hierarchical self organizing mapping method is done in a better way as compared to other algorithms.  ...  There are various methods of clustering and thresholding which have been proposed in this paper such as otsu , region growing , K Means , fuzzy c means and Hierarchical self organizing mapping algorithm  ...  The hierarchical self organizing map has been used for multi scale image segmentation. The combination of self organization and graphic mapping technique is known as HSOM.  ... 
doi:10.9790/2834-560813 fatcat:pfzdbx465fhuxd7bocjv7i4prm

GraphSIF: analyzing flow of payments in a Business-to-Business network to detect supplier impersonation

Rémi Canillas, Omar Hasan, Laurent Sarrat, Lionel Brunie
2020 Applied Network Science  
We propose to use a graph-based approach to design an Anomaly Detection System (ADS) based on a Self-Organizing Map (SOM) allowing us to label a suspicious transaction as either legitimate or fraudulent  ...  Supplier Impersonation Fraud (SIF) is a rising issue for Business-to-Business companies. The use of remote and quick digital transactions has made the task of identifying fraudsters more difficult.  ...  Acknowledgements The authors would like to acknowledge the help of the development team of SiS-id : François Agier & Kévin Lainte.  ... 
doi:10.1007/s41109-020-00283-1 fatcat:7esnki2tbnd6lpebrdtrjy72o4

Self-Organizing Maps for Structured Domains: Theory, Models, and Learning of Kernels [chapter]

Fabio Aiolli, Giovanni Da San Martino, Markus Hagenbuchner, Alessandro Sperduti
2009 Studies in Computational Intelligence  
Conclusions In this chapter, we have presented Self-Organizing Maps for processing of structured data.  ...  This chapter gives an overview of Self-Organizing methods capable of dealing directly with graph structured information for both, node focused and graph focused applications.  ... 
doi:10.1007/978-3-642-04003-0_2 fatcat:kbsdp5fjvjfijm2cdittt44ldi

Visualizing the Topical Structure of the Medical Sciences: A Self-Organizing Map Approach

André Skupin, Joseph R. Biberstine, Katy Börner, Matthias Dehmer
2013 PLoS ONE  
While self-organizing maps have been used for document visualization for some time, (1) little is known about how to deal with truly large document collections in conjunction with a large number of SOM  ...  We implement a high-resolution visualization of the medical knowledge domain using the self-organizing map (SOM) method, based on a corpus of over two million publications.  ...  Acknowledgments The work of Marilyn Stowell in digitizing expert annotations is gratefully acknowledged, as are the comments and suggestions made by Kevin Boyack in response to an earlier draft of this  ... 
doi:10.1371/journal.pone.0058779 pmid:23554924 pmcid:PMC3595294 fatcat:wrbglk4xvraznar5szvbnpnq54

Clustering XML Documents Using Self-organizing Maps for Structures [chapter]

M. Hagenbuchner, A. Sperduti, A. C. Tsoi, F. Trentini, F. Scarselli, M. Gori
2006 Lecture Notes in Computer Science  
Self-Organizing Maps capable of encoding structured information will be used for the clustering of XML documents. Documents formatted in XML are appropriately represented as graph data structures.  ...  It will be shown that the Self-Organizing Maps can be trained in an unsupervised fashion to group XML structured data into clusters, and that this task is scaled in linear time with increasing size of  ...  Acknowledgments The work presented in this paper received financial support from the Australian Research Council in form of a Linkage International Grant and a Discovery Project grant.  ... 
doi:10.1007/978-3-540-34963-1_37 fatcat:uijjux3atbfdzke3674v5onkza
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