C4.5: programs for machine learning
C4.5: programs for machine learning
Theory refinement combining analytical and empirical methods
Artificial Intelligence
Evaluation and Selection of Biases in Machine Learning
Machine Learning - Special issue on bias evaluation and selection
Top-Down Induction of Clustering Trees
ICML '98 Proceedings of the Fifteenth International Conference on Machine Learning
Efficient Construction of Comprehensible Hierarchical Clusterings
PKDD '98 Proceedings of the Second European Symposium on Principles of Data Mining and Knowledge Discovery
Assessment of Selection Restrictions Acquisition
SBIA '02 Proceedings of the 16th Brazilian Symposium on Artificial Intelligence: Advances in Artificial Intelligence
On Discovery of Extremely Low-Dimensional Clusters Using Semi-Supervised Projected Clustering
ICDE '05 Proceedings of the 21st International Conference on Data Engineering
MDAI'10 Proceedings of the 7th international conference on Modeling decisions for artificial intelligence
Semi-supervised agglomerative hierarchical clustering with ward method using clusterwise tolerance
MDAI'11 Proceedings of the 8th international conference on Modeling decisions for artificial intelligence
An extension of self-organizing maps to categorical data
EPIA'05 Proceedings of the 12th Portuguese conference on Progress in Artificial Intelligence
On text mining algorithms for automated maintenance of hierarchical knowledge directory
KSEM'06 Proceedings of the First international conference on Knowledge Science, Engineering and Management
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The capability of making use of existing prior knowledge is an important challenge for Knowledge Discovery tasks. As an unsupervised learning task, clustering appears to be one of the tasks that more benefits might obtain from prior knowledge. In this paper, we propose a method for providing declarative prior knowledge to a hierarchical clustering system stressing the interactive component. Preliminary results suggest that declarative knowledge is a powerful bias in order to improve the quality of clustering in domains were the internal biases of the system are inappropriate or there is not enough evidence in data and that it can lead the system to build more comprehensible clusterings.