Constant interaction-time scatter/gather browsing of very large document collections
SIGIR '93 Proceedings of the 16th annual international ACM SIGIR conference on Research and development in information retrieval
A practical web-based approach to generating topic hierarchy for text segments
Proceedings of the thirteenth ACM international conference on Information and knowledge management
The SMART Retrieval System—Experiments in Automatic Document Processing
The SMART Retrieval System—Experiments in Automatic Document Processing
Automatically labeling hierarchical clusters
dg.o '06 Proceedings of the 2006 international conference on Digital government research
Clustering quality measures for data samples with multiple labels
DBA'06 Proceedings of the 24th IASTED international conference on Database and applications
IEA/AIE'10 Proceedings of the 23rd international conference on Industrial engineering and other applications of applied intelligent systems - Volume Part III
IEA/AIE'11 Proceedings of the 24th international conference on Industrial engineering and other applications of applied intelligent systems conference on Modern approaches in applied intelligence - Volume Part I
WSOM'11 Proceedings of the 8th international conference on Advances in self-organizing maps
A new efficient and unbiased approach for clustering quality evaluation
PAKDD'11 Proceedings of the 15th international conference on New Frontiers in Applied Data Mining
Classifying French verbs using French and English lexical resources
ACL '12 Proceedings of the 50th Annual Meeting of the Association for Computational Linguistics: Long Papers - Volume 1
| Hi-index | 0.00 |
Hyperbolic visualization represents a useful tool for the interpretation of complex data analysis results, whenever it can be combined with efficient labeling strategies. In this paper, we firstly present a new approach combining original hypertree construction techniques for multidimensional clustering results visualization with novel cluster labeling techniques based on the use of cluster content evaluation criteria, like the F-measure on cluster properties. The first part of the paper briefly presents the cluster hypertree construction principle. The main part of the paper focuses on the presentation of the labeling techniques. It illustrates that the scope of the proposed techniques can be extended from single cluster labeling to labeling of hierarchical structures, like hypertrees. Finally, using specific evaluation criteria, we show the better efficiency of the proposed methods, as compared to usual labeling methods, both for single cluster labeling and for hierarchical labeling. The experimental context of the paper is a bibliographic database of 2127 PASCAL references related to the geological domain.