ACM Computing Surveys (CSUR)
PicSOM—content-based image retrieval with self-organizing maps
Pattern Recognition Letters - Selected papers from the 11th scandinavian conference on image analysis
Visual Explorations in Finance
Visual Explorations in Finance
Self-Organizing Maps
HD-Eye: Visual Mining of High-Dimensional Data
IEEE Computer Graphics and Applications
2D Maps for Visual Analysis and Retrieval in Large Multi-Feature 3D Model Databases
VIS '04 Proceedings of the conference on Visualization '04
Data Mining: Concepts and Techniques
Data Mining: Concepts and Techniques
A Visualization System for Space-Time and Multivariate Patterns (VIS-STAMP)
IEEE Transactions on Visualization and Computer Graphics
Similarity Search in Trajectory Databases
TIME '07 Proceedings of the 14th International Symposium on Temporal Representation and Reasoning
Visualizing the History of Living Spaces
IEEE Transactions on Visualization and Computer Graphics
Trajectory-based visual analysis of large financial time series data
ACM SIGKDD Explorations Newsletter - Special issue on visual analytics
Visual analytics tools for analysis of movement data
ACM SIGKDD Explorations Newsletter - Special issue on visual analytics
A clustering-based approach for discovering interesting places in trajectories
Proceedings of the 2008 ACM symposium on Applied computing
Class visualization of high-dimensional data with applications
Computational Statistics & Data Analysis
A visual digital library approach for time-oriented scientific primary data
ECDL'10 Proceedings of the 14th European conference on Research and advanced technology for digital libraries
Application notes: dynamic physical behavior analysis for financial trading decision support
IEEE Computational Intelligence Magazine
Proceedings of the 11th annual international ACM/IEEE joint conference on Digital libraries
Editorial: Challenging problems of geospatial visual analytics
Journal of Visual Languages and Computing
A discussion on visual interactive data exploration using self-organizing maps
WSOM'11 Proceedings of the 8th international conference on Advances in self-organizing maps
Bankruptcy trajectory analysis on french companies using self-organizing map
EPIA'11 Proceedings of the 15th Portugese conference on Progress in artificial intelligence
Efficient annotation of image data sets for computer vision applications
Proceedings of the 1st International Workshop on Visual Interfaces for Ground Truth Collection in Computer Vision Applications
Clustering and visualization of bankruptcy trajectory using self-organizing map
Expert Systems with Applications: An International Journal
Space-in-time and time-in-space self-organizing maps for exploring spatiotemporal patterns
EuroVis'10 Proceedings of the 12th Eurographics / IEEE - VGTC conference on Visualization
A visual analytics framework for spatio-temporal analysis and modelling
Data Mining and Knowledge Discovery
Visual Analytics for model-based medical image segmentation: Opportunities and challenges
Expert Systems with Applications: An International Journal
Opening up the "black box" of medical image segmentation with statistical shape models
The Visual Computer: International Journal of Computer Graphics
A visual exploration of mobile phone users, land cover, time, and space
Pervasive and Mobile Computing
TrajectoryLenses - a set-based filtering and exploration technique for long-term trajectory data
EuroVis '13 Proceedings of the 15th Eurographics Conference on Visualization
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Visual-interactive cluster analysis provides valuable tools for effectively analyzing large and complex data sets. Owing to desirable properties and an inherent predisposition for visualization, the Kohonen Feature Map (or Self-Organizing Map or SOM) algorithm is among the most popular and widely used visual clustering techniques. However, the unsupervised nature of the algorithm may be disadvantageous in certain applications. Depending on initialization and data characteristics, cluster maps (cluster layouts) may emerge that do not comply with user preferences, expectations or the application context. Considering SOM-based analysis of trajectory data, we propose a comprehensive visual-interactive monitoring and control framework extending the basic SOM algorithm. The framework implements the general Visual Analytics idea to effectively combine automatic data analysis with human expert supervision. It provides simple, yet effective facilities for visually monitoring and interactively controlling the trajectory clustering process at arbitrary levels of detail. The approach allows the user to leverage existing domain knowledge and user preferences, arriving at improved cluster maps. We apply the framework on several trajectory clustering problems, demonstrating its potential in combining both unsupervised (machine) and supervised (human expert) processing, in producing appropriate cluster results.