scholarly journals Clustering Large, Multi-level Data Sets: An Approach Based on Kohonen Self Organizing Maps

2000 ◽  
pp. 353-358 ◽  
Author(s):  
Antonio Ciampi ◽  
Yves Lechevallier
Keyword(s):  
Data Sets ◽  
Self Organizing Maps ◽  
Level Data ◽  
Multi Level ◽  
Self Organizing

scholarly journals Evolution of SOMs’ Structure and Learning Algorithm: From Visualization of High-Dimensional Data to Clustering of Complex Data

Algorithms ◽  
2020 ◽  
Vol 13 (5) ◽  
pp. 109 ◽  
Author(s):  
Marian B. Gorzałczany ◽  
Filip Rudziński
Keyword(s):  
Data Visualization ◽  
Data Clustering ◽  
Learning Algorithm ◽  
High Dimensional Data ◽  
High Dimensional ◽  
Data Sets ◽  
Complex Data ◽  
Self Organizing Maps ◽  
Grid Networks ◽  
Self Organizing

In this paper, we briefly present several modifications and generalizations of the concept of self-organizing neural networks—usually referred to as self-organizing maps (SOMs)—to illustrate their advantages in applications that range from high-dimensional data visualization to complex data clustering. Starting from conventional SOMs, Growing SOMs (GSOMs), Growing Grid Networks (GGNs), Incremental Grid Growing (IGG) approach, Growing Neural Gas (GNG) method as well as our two original solutions, i.e., Generalized SOMs with 1-Dimensional Neighborhood (GeSOMs with 1DN also referred to as Dynamic SOMs (DSOMs)) and Generalized SOMs with Tree-Like Structures (GeSOMs with T-LSs) are discussed. They are characterized in terms of (i) the modification mechanisms used, (ii) the range of network modifications introduced, (iii) the structure regularity, and (iv) the data-visualization/data-clustering effectiveness. The performance of particular solutions is illustrated and compared by means of selected data sets. We also show that the proposed original solutions, i.e., GeSOMs with 1DN (DSOMs) and GeSOMS with T-LSs outperform alternative approaches in various complex clustering tasks by providing up to 20 % increase in the clustering accuracy. The contribution of this work is threefold. First, algorithm-oriented original computer-implementations of particular SOM’s generalizations are developed. Second, their detailed simulation results are presented and discussed. Third, the advantages of our earlier-mentioned original solutions are demonstrated.

Estimating the Number of Clusters in Multivariate Data by Self-Organizing Maps

1999 ◽  
Vol 09 (03) ◽  
pp. 195-202 ◽  
Author(s):  
JOSÉ ALFREDO FERREIRA COSTA ◽  
MÁRCIO LUIZ DE ANDRADE NETTO
Keyword(s):  
A Priori ◽  
Multivariate Data ◽  
Feature Space ◽  
Search Space ◽  
Data Sets ◽  
Self Organizing Maps ◽  
Data Set ◽  
Number Of Clusters ◽  
Using Data ◽  
Self Organizing

Determining the structure of data without prior knowledge of the number of clusters or any information about their composition is a problem of interest in many fields, such as image analysis, astrophysics, biology, etc. Partitioning a set of n patterns in a p-dimensional feature space must be done such that those in a given cluster are more similar to each other than the rest. As there are approximately [Formula: see text] possible ways of partitioning the patterns among K clusters, finding the best solution is very hard when n is large. The search space is increased when we have no a priori number of partitions. Although the self-organizing feature map (SOM) can be used to visualize clusters, the automation of knowledge discovery by SOM is a difficult task. This paper proposes region-based image processing methods to post-processing the U-matrix obtained after the unsupervised learning performed by SOM. Mathematical morphology is applied to identify regions of neurons that are similar. The number of regions and their labels are automatically found and they are related to the number of clusters in a multivariate data set. New data can be classified by labeling it according to the best match neuron. Simulations using data sets drawn from finite mixtures of p-variate normal densities are presented as well as related advantages and drawbacks of the method.

THE COMBINED USE OF SELF-ORGANIZING MAPS AND ANDREWS' CURVES

2005 ◽  
Vol 15 (03) ◽  
pp. 197-206 ◽  
Author(s):  
C. GARCÍA-OSORIO ◽  
C. FYFE
Keyword(s):  
Data Sets ◽  
Analysis Tool ◽  
Use Of Self ◽  
Process Data ◽  
Self Organizing Maps ◽  
Combined Use ◽  
Exploratory Data ◽  
Data Analysis Tool ◽  
Analyze Data ◽  
Self Organizing

The use of self-organizing maps to analyze data often depends on finding effective methods to visualize the SOM's structure. In this paper we propose a new way to perform that visualization using a variant of Andrews' Curves. Also we show that the interaction between these two methods allows us to find sub-clusters within identified clusters. Perhaps more importantly, using the SOM to pre-process data by identifying gross features enables us to use Andrews' Curves on data sets which would have previously been too large for the methodology. Finally we show how a three way interaction between the human user and these two methods can be a valuable exploratory data analysis tool.

Supervised Self-Organizing Maps in Drug Discovery. 1. Robust Behavior with Overdetermined Data Sets

2005 ◽  
Vol 45 (6) ◽  
pp. 1749-1758 ◽  
Author(s):  
Yun-De Xiao ◽  
Aaron Clauset ◽  
Rebecca Harris ◽  
Ersin Bayram ◽  
Peter Santago ◽  
...  
Keyword(s):  
Drug Discovery ◽  
Data Sets ◽  
Self Organizing Maps ◽  
Self Organizing

scholarly journals Multi-Level Sensing Technologies in Landslide Research—Hrvatska Kostajnica Case Study, Croatia

Sensors ◽  
2021 ◽  
Vol 22 (1) ◽  
pp. 177
Author(s):  
Laszlo Podolszki ◽  
Ivan Kosović ◽  
Tomislav Novosel ◽  
Tomislav Kurečić
Keyword(s):  
Ground Surface ◽  
Surface Displacement ◽  
Data Sets ◽  
Level Data ◽  
Landslide Body ◽  
Aerial Vehicle ◽  
Multi Level ◽  
Geochemical Analyses ◽  
Landslide Model

In March 2018, a landslide in Hrvatska Kostajnica completely destroyed multiple households. The damage was extensive, and lives were endangered. The question remains: Can it happen again? To enhance the knowledge and understanding of the soil and rock behaviour before, during, and after this geo-hazard event, multi-level sensing technologies in landslide research were applied. Day after the event field mapping and unmanned aerial vehicle (UAV) data were collected with the inspection of available orthophoto and “geo” data. For the landslide, a new geological column was developed with mineralogical and geochemical analyses. The application of differential interferometric synthetic aperture radar (DInSAR) for detecting ground surface displacement was undertaken in order to determine pre-failure behaviour and to give indications about post-failure deformations. In 2020, electrical resistivity tomography (ERT) in the landslide body was undertaken to determine the depth of the landslide surface, and in 2021 ERT measurements in the vicinity of the landslide area were performed to obtain undisturbed material properties. Moreover, in 2021, detailed light detection and ranging (LIDAR) data were acquired for the area. All these different level data sets are being analyzed in order to develop a reliable landslide model as a first step towards answering the aforementioned question. Based on applied multi-level sensing technologies and acquired data, the landslide model is taking shape. However, further detailed research is still recommended.

scholarly journals Visual analysis of self-organizing maps

2011 ◽  
Vol 16 (4) ◽  
pp. 488-504 ◽  
Author(s):  
Pavel Stefanovič ◽  
Olga Kurasova
Keyword(s):  
Data Clustering ◽  
Visual Analysis ◽  
Data Sets ◽  
The European Union ◽  
Self Organizing Maps ◽  
Data Set ◽  
Economic Indices ◽  
Graphical Presentation ◽  
Iris Data ◽  
Self Organizing

In the article, an additional visualization of self-organizing maps (SOM) has been investigated. The main objective of self-organizing maps is data clustering and their graphical presentation. Opportunities of SOM visualization in four systems (NeNet, SOM-Toolbox, Databionic ESOM and Viscovery SOMine) have been investigated. Each system has its additional tools for visualizing SOM. A comparative analysis has been made for two data sets: Fisher’s iris data set and the economic indices of the European Union countries. A new SOM system is also introduced and researched. The system has a specific visualization tool. It is missing in other SOM systems. It helps to see the proportion of neurons, corresponding to the data items, belonging to the different classes, and fallen in the same SOM cell.

scholarly journals Trajectory Analysis on Spherical Self-Organizing Maps With Application to Gesture Recognition

2021 ◽  
Author(s):  
Artur Oliva Gonsales
Keyword(s):  
Gesture Recognition ◽  
Trajectory Analysis ◽  
Microsoft Kinect ◽  
Data Sets ◽  
Self Organizing Map ◽  
Self Organizing Maps ◽  
New Approach ◽  
Spatiotemporal Information ◽  
Full Body ◽  
Self Organizing

In this work, a new approach to gesture recognition using the properties of Spherical Self- Organizing Map (SSOM) is investigated. Bounded mapping of data onto a SSOM creates not only a powerful tool for visualization but also for modeling spatiotemporal information of gesture data. The SSOM allows for the automated decomposition of a variety of gestures into a set of distinct postures. The decomposition naturally organizes this set into a spatial map that preserves associations between postures, upon which we formalize the notion of a gesture as a trajectory through learned posture space. Trajectories from different gestures may share postures. However, the path traversed through posture space is relatively unique. Different variations of posture transitions occurring within a gesture trajectory are used to classify new unknown gestures. Four mechanisms for detecting the occurrence of a trajectory of an unknown gesture are proposed and evaluated on two data sets involving both hand gestures (public sign language database) and full body gestures (Microsoft Kinect database collected in-house) showing the effectiveness of the proposed approach.

scholarly journals Saviorganizuojančių neuroninių tinklų sistemų lyginamoji analizė

2009 ◽  
Vol 50 ◽  
pp. 334-339
Author(s):  
Pavel Stefanovič ◽  
Olga Kurasova
Keyword(s):  
Data Clustering ◽  
The Self ◽  
Data Sets ◽  
Self Organizing Map ◽  
Self Organizing Maps ◽  
Learning Rules ◽  
Main Target ◽  
Similarities And Differences ◽  
Graphical Presentation ◽  
Self Organizing

Straipsnyje nagrinėjamos ir lyginamos tarpusavyje trys saviorganizuojančių neuroninių tinklų (SOM) sistemos: NeNet, SOM-Toolbox ir Databionic ESOM. Pagrindinis šių sistemų tikslas yra suskirstyti duomenis į klasterius pagal jų panašumą, pateikti juos SOM žemėlapyje. Sistemos viena nuo kitos skiriasi duomenų pateikimu, mokymo taisyklėmis, vizualizavimo galimybėmis, todėl čia aptariami sistemų panašumai ir skirtumai. SOM žemėlapiams mokyti ir vizualizuoti naudojami irisų ir stikloduomenys.Comparative Analysis of Self-Organizing Map SystemsPavel Stefanovič, Olga Kurasova SummaryIn the article, we compare three systems of self-organizing maps: NeNet, SOM-Toolbox and Databionic ESOM. The main target of the usage of the systems is data clustering and their graphical presentation on the self-organizing map (SOM). The self-organizing maps are one of types of artifi cial neural networks. The SOM systems are different one from other in their interfaces, the data pre-processing, learning rules, visualization manners, etc. Similarities and differences of the systems have been highlighted here. The experiments have been carried out with two data sets: iris and glass. Quantization and topographic errors of SOMs have been estimated, too.an>

scholarly journals Trajectory Analysis on Spherical Self-Organizing Maps With Application to Gesture Recognition

2021 ◽  
Author(s):  
Artur Oliva Gonsales
Keyword(s):  
Gesture Recognition ◽  
Trajectory Analysis ◽  
Microsoft Kinect ◽  
Data Sets ◽  
Self Organizing Map ◽  
Self Organizing Maps ◽  
New Approach ◽  
Spatiotemporal Information ◽  
Full Body ◽  
Self Organizing

In this work, a new approach to gesture recognition using the properties of Spherical Self- Organizing Map (SSOM) is investigated. Bounded mapping of data onto a SSOM creates not only a powerful tool for visualization but also for modeling spatiotemporal information of gesture data. The SSOM allows for the automated decomposition of a variety of gestures into a set of distinct postures. The decomposition naturally organizes this set into a spatial map that preserves associations between postures, upon which we formalize the notion of a gesture as a trajectory through learned posture space. Trajectories from different gestures may share postures. However, the path traversed through posture space is relatively unique. Different variations of posture transitions occurring within a gesture trajectory are used to classify new unknown gestures. Four mechanisms for detecting the occurrence of a trajectory of an unknown gesture are proposed and evaluated on two data sets involving both hand gestures (public sign language database) and full body gestures (Microsoft Kinect database collected in-house) showing the effectiveness of the proposed approach.

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