An effective document clustering method using user-adaptable distance metrics
Proceedings of the 2002 ACM symposium on Applied computing
Inferring hierarchical descriptions
Proceedings of the eleventh international conference on Information and knowledge management
Constrained K-means Clustering with Background Knowledge
ICML '01 Proceedings of the Eighteenth International Conference on Machine Learning
Automatically labeling hierarchical clusters
dg.o '06 Proceedings of the 2006 international conference on Digital government research
Personalized Hierarchical Clustering
WI '06 Proceedings of the 2006 IEEE/WIC/ACM International Conference on Web Intelligence
Automatic extraction of clusters from hierarchical clustering representations
PAKDD'03 Proceedings of the 7th Pacific-Asia conference on Advances in knowledge discovery and data mining
Collection Browsing through Automatic Hierarchical Tagging
AH '08 Proceedings of the 5th international conference on Adaptive Hypermedia and Adaptive Web-Based Systems
SHACUN: semi-supervised hierarchical active clustering based on ranking constraints
ICDM'12 Proceedings of the 12th Industrial conference on Advances in Data Mining: applications and theoretical aspects
Machine Learning
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In order to organize huge document collections, labeled hierarchical structures are used frequently. Users are most efficient in navigating such hierarchies, if they reflect their personal interests. Thus, we propose in this article an approach that is able to derive a personalized hierarchical structure from a document collection. The approach is based on a semi-supervised hierarchical clustering approach, which is combined with a biased cluster extraction process. Furthermore, we label the clusters for efficient navigation. Besides the algorithms itself, we describe an evaluation of our approach using benchmark datasets.