C4.5: programs for machine learning
C4.5: programs for machine learning
Machine learning, neural and statistical classification
Machine learning, neural and statistical classification
The KDD process for extracting useful knowledge from volumes of data
Communications of the ACM
Visual classification: an interactive approach to decision tree construction
KDD '99 Proceedings of the fifth ACM SIGKDD international conference on 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
Data mining: concepts and techniques
Data mining: concepts and techniques
Visualizing multi-dimensional clusters, trends, and outliers using star coordinates
Proceedings of the seventh ACM SIGKDD international conference on Knowledge discovery and data mining
Partitioning Nominal Attributes in Decision Trees
Data Mining and Knowledge Discovery
Machine Learning
SLIQ: A Fast Scalable Classifier for Data Mining
EDBT '96 Proceedings of the 5th International Conference on Extending Database Technology: Advances in Database Technology
Situation-Aware adaptive visualization for sensory data stream mining
Sensor-KDD'08 Proceedings of the Second international conference on Knowledge Discovery from Sensor Data
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In this paper, we demonstrate JRV, a new data mining visualization tool for the knowledge discovery process where the user and computer can cooperate with each other. First, the computer can be instructed by the user interactively to compute values of several evaluation functions. Then, the user can take advantage of domain knowledge and assess the intermediate results obtained. Furthermore, by providing effective and efficient data visualization, the pattern recognition capacities of users can be greatly improved. Instead of being limited to two attributes at a given time in independence diagrams, this novel tool will allow simultaneous analyses of multiple attribute dependencies using four different drawing panels. Also, by utilizing the existing techniques of data visualization, we design a general model which can handle both categorical and numerical attributes in an intuitive way. With this model, we can identify patterns of interests efficiently. Through actual examples, we show that it might help users to find novel attribute relationships. This work is supported by NIH grant #RO1-CA98932-01.