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
Growing a tree classifier with imprecise data
Pattern Recognition Letters
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
Analysis of Symbolic Data: Exploratory Methods for Extracting Statistical Information from Complex Data
Guest Editor's Introduction: Visual Data Mining
IEEE Computer Graphics and Applications
Recursive Pattern: A Technique for Visualizing Very Large Amounts of Data
VIS '95 Proceedings of the 6th conference on Visualization '95
Inventing discovery tools: combining information visualization with data mining
Information Visualization
SVM and Graphical Algorithms: A Cooperative Approach
ICDM '04 Proceedings of the Fourth IEEE International Conference on Data Mining
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We present new visual data mining algorithms for interactive decision tree construction with large datasets. The size of data stored in the world is constantly increasing but the limits of current visual data mining (and visualization) methods concerning the number of items and dimensions of the dataset treated are well known (even with pixellisation methods). One solution to improve these methods is to use a higher-level representation of the data, for example a symbolic data representation. Our new interactive decision tree construction algorithms deal with interval and taxonomical data. With such a representation, we are able to deal with potentially very large datasets because we do not use the original data but higher-level data representation. Interactive algorithms are examples of new data mining approach aiming at involving more intensively the user in the process. The main advantages of this user-centered approach are the increased confidence and comprehensibility of the obtained model, because the user was involved in its construction and the possible use of human pattern recognition capabilities. We present some results we obtained on very large datasets.