C4.5: programs for machine learning
C4.5: programs for machine learning
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
Classification and visualization for high-dimensional data
Proceedings of the sixth ACM SIGKDD international conference on Knowledge discovery and data mining
Proximal support vector machine classifiers
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
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
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
Decision trees: a recent overview
Artificial Intelligence Review
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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 assets from multiple sources. This paper presents two graphical interactive decision tree construction algorithms able to deal either with (usual) continuous data or with interval and taxonomical data. They are the extensions of two existing algorithms: CIAD [17] and PBC [3]. Both CIAD and PBC algorithms can be used in an interactive or cooperative mode (with an automatic algorithm to find the best split of the current tree node). We have modified the corresponding help mechanisms to allow them to deal with interval-valued attributes. Some of the results obtained on interval-valued and taxonomical data sets are presented with the methods we have used to create these data sets.