The grand tour: a tool for viewing multidimensional data
SIAM Journal on Scientific and Statistical Computing
C4.5: programs for machine learning
C4.5: programs for machine learning
The nature of statistical learning theory
The nature of statistical learning theory
The quickhull algorithm for convex hulls
ACM Transactions on Mathematical Software (TOMS)
Advances in knowledge discovery and data mining
Advances in knowledge discovery and data mining
Towards an effective cooperation of the user and the computer for classification
Proceedings of the sixth ACM SIGKDD international conference on Knowledge discovery and data mining
Gaining insights into support vector machine pattern classifiers using projection-based tour methods
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
Interactive machine learning: letting users build classifiers
International Journal of Human-Computer Studies
Guest Editor's Introduction: Visual Data Mining
IEEE Computer Graphics and Applications
Redefining Clustering for High-Dimensional Applications
IEEE Transactions on Knowledge and Data Engineering
A parallel mixture of SVMs for very large scale problems
Neural Computation
SLIQ: A Fast Scalable Classifier for Data Mining
EDBT '96 Proceedings of the 5th International Conference on Extending Database Technology: Advances in Database Technology
Duality and Geometry in SVM Classifiers
ICML '00 Proceedings of the Seventeenth International Conference on Machine Learning
Interactive Construction of Decision Trees
PAKDD '01 Proceedings of the 5th Pacific-Asia Conference on Knowledge Discovery and Data Mining
Inventing discovery tools: combining information visualization with data mining
Information Visualization
A Feature Selection Newton Method for Support Vector Machine Classification
Computational Optimization and Applications
SVM and Graphical Algorithms: A Cooperative Approach
ICDM '04 Proceedings of the Fourth IEEE International Conference on Data Mining
Design and evaluation of visualization support to facilitate decision trees classification
International Journal of Human-Computer Studies
A system for induction of oblique decision trees
Journal of Artificial Intelligence Research
An introduction to kernel-based learning algorithms
IEEE Transactions on Neural Networks
Visual exploration of classification models for various data types in risk assessment
Information Visualization - Special issue on Best Papers of Visual Analytics Science and Technology (VAST) 2010
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Visual data-mining strategy lies in tightly coupling the visualizations and analytical processes into one data-mining tool that takes advantage of the strengths from multiple sources. We present concrete cooperation between automatic algorithms, interactive algorithms and visualization methods. The first kind of cooperation is an interactive decision tree algorithm CIAD. It allows the user to be helped by an automatic algorithm based on a support vector machine (SVM) to optimize the interactive split performed in the current tree node or to compute the best split in an automatic mode. Another effective cooperation is a visualization algorithm used to explain the results of SVM algorithm. The same visualization method can also be used to help the user in the parameters tuning step in input of automatic SVM algorithms. Then we present methods using both automatic and interactive methods to deal with very large datasets. The obtained results let us think it is a promising way to deal with very large datasets.