Combinatorial optimization: algorithms and complexity
Combinatorial optimization: algorithms and complexity
Clustering through decision tree construction
Proceedings of the ninth international conference on Information and knowledge management
Top-Down Induction of Clustering Trees
ICML '98 Proceedings of the Fifteenth International Conference on Machine Learning
Interpretable Hierarchical Clustering by Constructing an Unsupervised Decision Tree
IEEE Transactions on Knowledge and Data Engineering
Initializing K-means Batch Clustering: A Critical Evaluation of Several Techniques
Journal of Classification
Probabilistic distance clustering adjusted for cluster size
Probability in the Engineering and Informational Sciences
Hierarchical density-based clustering of categorical data and a simplification
PAKDD'07 Proceedings of the 11th Pacific-Asia conference on Advances in knowledge discovery and data mining
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We herein introduce a new method of interpretable clustering that uses unsupervised binary trees. It is a three-stage procedure, the first stage of which entails a series of recursive binary splits to reduce the heterogeneity of the data within the new subsamples. During the second stage (pruning), consideration is given to whether adjacent nodes can be aggregated. Finally, during the third stage (joining), similar clusters are joined together, even if they do not share the same parent originally. Consistency results are obtained, and the procedure is used on simulated and real data sets.