Anmerkung: Intro -- Preface -- Organization -- Steering Committee -- Program Committee -- Contents -- Self-organizing Maps: Theoretical Developments -- Look and Feel What and How Recurrent Self-Organizing Maps Learn -- 1 Introduction -- 2 Methods -- 2.1 Algorithm -- 2.2 Representations -- 2.3 Evaluation -- 3 Results -- 3.1 Ambiguous Observations -- 3.2 Long Term Dependencies -- 3.3 Adapting to a Changing Dynamics -- 3.4 Noisy Observations -- 3.5 Perturbed by a Noise State -- 4 Conclusion -- References -- Self-Organizing Mappings on the Flag Manifold -- 1 Introduction -- 2 Introduction to Flag Manifold with Data Analysis Examples -- 3 Numerical Representation and Geodesics -- 3.1 Flag Manifold -- 3.2 Geodesic and Distance Between Two Points on Flag Manifold -- 3.3 Iterative Alternating Algorithm -- 4 SOM on Flag Manifolds -- 4.1 Numerical Experiment -- 5 Conclusions and Future Work -- References -- Self-Organizing Maps with Convolutional Layers -- 1 Introduction -- 2 Self-Organizing Maps -- 3 Related Work -- 4 Convolutional Layers -- 5 SOM with Convolutional Layers -- 6 Quality Measures -- 6.1 Kruskal Shepard Error -- 6.2 Cross Entropy -- 6.3 Minor Class Occurrence -- 6.4 Class Scatter Index -- 7 Experimental Analysis -- 7.1 Experimental Settings -- 7.2 Quality Measure Results -- 7.3 Visualization Results -- 8 Conclusion -- References -- Cellular Self-Organising Maps - CSOM -- 1 Introduction -- 2 Self-Organising Maps: SOM and Cellular SOM -- 2.1 SOM: Self-Organising Maps -- 2.2 CSOM: Cellular Self-Organising Maps -- 2.3 Algorithms -- 3 Experimental Setup and Results -- 3.1 Quantisation of Artificial d-dimensional Distributions -- 3.2 Video Compression -- 4 Conclusion -- References -- A Probabilistic Method for Pruning CADJ Graphs with Applications to SOM Clustering -- 1 Introduction: The CADJ Graph -- 2 A Probabilistic Model for CADJ -- 3 A Multi-focal View. , 4 The Metric -- 5 Ranking Connections for Removal -- 6 Clustering Applications -- 6.1 6d Synthetic Spectral Image -- 6.2 Real Data: Ocean City Spectral Image -- 7 Conclusions and Outlook -- References -- Practical Applications of Self-Organizing Maps, Learning Vector Quantization and Clustering -- SOM-Based Anomaly Detection and Localization for Space Subsystems -- Abstract -- 1 Introduction -- 2 Related Work -- 3 Self-Organizing Map Background -- 4 Methods -- 4.1 Data Processing -- 4.2 Anomaly Detection via MQE -- 4.3 Anomaly Localization via Supervised Feature Extraction -- 5 Experiments and Discussion -- 5.1 Data Collection -- 5.2 Anomaly Detection Analysis -- 5.3 Anomaly Localization Analysis -- 6 Conclusions and Future Work -- References -- Self-Organizing Maps in Earth Observation Data Cubes Analysis -- 1 Introduction -- 2 Land Use and Cover Change Information from Earth Observation Data Cubes -- 2.1 Earth Observation Satellite Image Time Series -- 2.2 Vegetation Indexes -- 2.3 Using SOM to Improve the Quality of Land Use and Cover Samples -- 3 Case Study -- 4 Final Remarks -- References -- Competencies in Higher Education: A Feature Analysis with Self-Organizing Maps -- Abstract -- 1 Introduction -- 2 State of the Art -- 3 Materials and Methods -- 3.1 Training Dataset -- 3.2 Clustering Students and Obtaining Main Features -- 4 Results -- 5 Conclusions and Future Works -- References -- Using SOM-Based Visualization to Analyze the Financial Performance of Consumer Discretionary Firms -- Abstract -- 1 Introduction -- 2 Literature Review -- 3 Methodology -- 4 Results -- 5 Discussion -- 6 Conclusion -- References -- Novelty Detection with Self-Organizing Maps for Autonomous Extraction of Salient Tracking Features -- 1 Introduction -- 2 Image Representation with SOM -- 2.1 Self-Organizing Maps -- 2.2 Image Representation -- 3 Dynamic Neural Fields. , 4 Our Tracking Application -- 5 Results -- 6 Conclusion -- References -- Robust Adaptive SOMs Challenges in a Varied Datasets Analytics -- Abstract -- 1 Introduction -- 2 SOM Algorithm -- 3 RA-SOM Algorithm -- 4 Simulation Results -- 4.1 Balance Dataset -- 4.2 Dermatology Dataset -- 4.3 Arcene Dataset -- 4.4 Gisette Dataset -- 5 Conclusion and Future Work -- References -- Detection of Abnormal Flights Using Fickle Instances in SOM Maps -- 1 Introduction -- 2 The Data -- 3 First Level of Labeling -- 4 Two-Levels Clustering and Resulting Labels -- 5 Dissimilarity Matrix and Relational SOM -- 5.1 Substitutions Costs -- 5.2 Adding Costs and Deletion Costs -- 6 Clustering the Labeled Sequences and Identifying Fickle Flights -- 7 Conclusion -- References -- LVQ-type Classifiers for Condition Monitoring of Induction Motors: A Performance Comparison -- 1 Introduction -- 2 Basics of Cluster Validation Techniques -- 2.1 Cluster Validity Indices -- 3 Prototype-Based Classifiers -- 3.1 LVQ Classifiers -- 4 Results and Discussion -- 5 Conclusions and Further Work -- References -- When Clustering the Multiscalar Fingerprint of the City Reveals Its Segregation Patterns -- 1 Introduction -- 2 Building a Multiscalar Fingerprint of the City -- 3 Focal Distances and Distortion Coefficients -- 4 Clustering Trajectories -- 4.1 Defining Contrasts and Indices of Features Importance -- 4.2 Five hotspots of Segregation for the City of Paris -- 5 Conclusion and Perspectives -- References -- Using Hierarchical Clustering to Understand Behavior of 3D Printer Sensors -- Abstract -- 1 Introduction -- 1.1 3D Printing Overview -- 1.2 Data Collection and Parsing -- 1.3 Data Preprocessing -- 2 Statistical Clustering Method -- 3 Interpretation of Clusters -- 3.1 Analysis of Conditional Distributions Versus Classes -- 3.2 Detection of Non-informative and Redundant Variables. , 3.3 Pattern Conceptualization -- 4 Conclusion -- References -- A Walk Through Spectral Bands: Using Virtual Reality to Better Visualize Hyperspectral Data -- 1 Introduction -- 2 Background -- 2.1 Hyperspectral Data -- 2.2 Virtual Reality for Data Visualization -- 3 Example Visualizations -- 3.1 Indian Pines -- 3.2 Chemical Plume Detection -- 4 Conclusion -- References -- Incremental Traversability Assessment Learning Using Growing Neural Gas Algorithm -- 1 Introduction -- 2 Problem Specification -- 3 Evaluation Results -- 4 Conclusion -- References -- Learning Vector Quantization: Theoretical Developments -- Investigation of Activation Functions for Generalized Learning Vector Quantization -- 1 Introduction -- 2 Generalized Learning Vector Quantization - A Multilayer Network Perspective -- 2.1 Basics of GLVQ -- 2.2 GLVQ - A Neural Network Perspective -- 2.3 Activation Function for MLP and GLVQ-MLN -- 3 Numerical Results for Activation Functions in GLVQ -- 3.1 Data Sets -- 3.2 Results -- 4 Conclusions -- References -- Robustness of Generalized Learning Vector Quantization Models Against Adversarial Attacks -- 1 Introduction -- 2 Learning Vector Quantization -- 3 Experimental Setup -- 3.1 Adversarial Attacks -- 3.2 Robustness Metrics -- 3.3 Training Setup and Models -- 4 Results -- 5 Conclusion -- References -- Passive Concept Drift Handling via Momentum Based Robust Soft Learning Vector Quantization -- 1 Introduction -- 2 Related Work -- 3 Streaming Data and Concept Drift -- 3.1 Concept Drift -- 4 Robust Soft Learning Vector Quantization -- 4.1 Momentum Based Optimization -- 5 Experiments -- 5.1 Results -- 6 Conclusion -- References -- Prototype-Based Classifiers in the Presence of Concept Drift: A Modelling Framework -- 1 Introduction -- 2 Models and Methods -- 2.1 Learning Vector Quantization -- 2.2 The Dynamics of LVQ. , 2.3 LVQ Dynamics Under Concept Drift -- 3 Results and Discussion -- 4 Summary and Outlook -- References -- Theoretical Developments in Clustering, Deep Learning and Neural Gas -- Soft Subspace Topological Clustering over Evolving Data Stream -- 1 Introduction -- 2 Model Proposition -- 3 Experimental Evaluation -- 4 Conclusion -- References -- Solving a Tool-Based Interaction Task Using Deep Reinforcement Learning with Visual Attention -- 1 Introduction -- 2 Reinforcement Learning -- 2.1 The REINFORCE Algorithm -- 3 The RAA3C MODEL -- 3.1 The Location Network -- 3.2 Glimpse Network -- 3.3 Context Network -- 3.4 The Actor-Critic Network -- 3.5 Training -- 4 Learning Domain -- 5 Experiments/Results -- 6 Conclusion -- References -- Approximate Linear Dependence as a Design Method for Kernel Prototype-Based Classifiers -- 1 Introduction -- 2 Basics of Prototype-Based Classification -- 2.1 Kernel Functions -- 3 The Proposed Approach -- 4 Results and Discussion -- 4.1 Initial Tests -- 4.2 More General Tests -- 5 Conclusions and Further Work -- References -- Subspace Quantization on the Grassmannian -- 1 Introduction -- 2 The Grassmannian -- 3 Averaging Subspaces -- 4 Grassmann K-means Algorithm -- 5 The LBG Algorithm on the Grassmannian -- 6 Numerical Experiments -- 6.1 MNIST Results -- 6.2 Indian Pines Results -- 7 Conclusions -- References -- Variants of Fuzzy Neural Gas -- 1 Introduction -- 2 Interpretation of Distance for Different Types of Data -- 3 Possibilistic Fuzzy c-Means -- 4 Possibilistic Fuzzy Neural Gas -- 4.1 Vectorial Data -- 4.2 Relational Data -- 4.3 Median Data -- 4.4 Remarks -- 5 Experiments -- 5.1 Artificial Gaussian Distributions -- 5.2 Clustering Transcripts of Psychotherapy Sessions -- 6 Conclusion -- References -- Autoencoders Covering Space as a Life-Long Classifier -- 1 Introduction -- 2 Related Work -- 3 Analysis -- 4 Method. , 5 Results.