The KDD process for extracting useful knowledge from volumes of data
Communications of the ACM
Self-organizing maps
GTM: the generative topographic mapping
Neural Computation
A framework for measuring changes in data characteristics
PODS '99 Proceedings of the eighteenth ACM SIGMOD-SIGACT-SIGART symposium on Principles of database systems
Visualizing multi-dimensional clusters, trends, and outliers using star coordinates
Proceedings of the seventh ACM SIGKDD international conference on Knowledge discovery and data mining
A human-computer cooperative system for effective high dimensional clustering
Proceedings of the seventh ACM SIGKDD international conference on Knowledge discovery and data mining
Towards effective and interpretable data mining by visual interaction
ACM SIGKDD Explorations Newsletter
Visual Explorations in Finance
Visual Explorations in Finance
Content-based organization and visualization of music archives
Proceedings of the tenth ACM international conference on Multimedia
Using Smoothed Data Histograms for Cluster Visualization in Self-Organizing Maps
ICANN '02 Proceedings of the International Conference on Artificial Neural Networks
In Search of the Horowitz Factor: Interim Report on a Musical Discovery Project
DS '02 Proceedings of the 5th International Conference on Discovery Science
On interactive visualization of high-dimensional data using the hyperbolic plane
Proceedings of the eighth ACM SIGKDD international conference on Knowledge discovery and data mining
IJCNN '00 Proceedings of the IEEE-INNS-ENNS International Joint Conference on Neural Networks (IJCNN'00)-Volume 5 - Volume 5
An Intuitive Framework for Understanding Changes in Evolving Data Streams
ICDE '02 Proceedings of the 18th International Conference on Data Engineering
Visualizing Expressive Performance in Tempo-Loudness Space
Computer Music Journal
Psychoacoustics: Facts and Models
Psychoacoustics: Facts and Models
Self organization of a massive document collection
IEEE Transactions on Neural Networks
Clustering of the self-organizing map
IEEE Transactions on Neural Networks
Semisupervised Learning of Hierarchical Latent Trait Models for Data Visualization
IEEE Transactions on Knowledge and Data Engineering
Exploring Music Collections by Browsing Different Views
Computer Music Journal
A Prediction-Based Visual Approach for Cluster Exploration and Cluster Validation by HOV3
PKDD 2007 Proceedings of the 11th European conference on Principles and Practice of Knowledge Discovery in Databases
A Visual Method for High-Dimensional Data Cluster Exploration
ICONIP '09 Proceedings of the 16th International Conference on Neural Information Processing: Part II
HOV3: an approach to visual cluster analysis
ADMA'06 Proceedings of the Second international conference on Advanced Data Mining and Applications
Center-Wise intra-inter silhouettes
SUM'12 Proceedings of the 6th international conference on Scalable Uncertainty Management
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Using visualization techniques to explore and understand high-dimensional data is an efficient way to combine human intelligence with the immense brute force computation power available nowadays. Several visualization techniques have been developed to study the cluster structure of data, i.e., the existence of distinctive groups in the data and how these clusters are related to each other. However, only few of these techniques lend themselves to studying how this structure changes if the features describing the data are changed. Understanding this relationship between the features and the cluster structure means understanding the features themselves and is thus a useful tool in the feature extraction phase.In this paper we present a novel approach to visualizing how modification of the features with respect to weighting or normalization changes the cluster structure. We demonstrate the application of our approach in two music related data mining projects.