Fast and effective text mining using linear-time document clustering
KDD '99 Proceedings of the fifth ACM SIGKDD international conference on Knowledge discovery and data mining
Evaluation of hierarchical clustering algorithms for document datasets
Proceedings of the eleventh international conference on Information and knowledge management
M-tree: An Efficient Access Method for Similarity Search in Metric Spaces
VLDB '97 Proceedings of the 23rd International Conference on Very Large Data Bases
Independent Quantization: An Index Compression Technique for High-Dimensional Data Spaces
ICDE '00 Proceedings of the 16th International Conference on Data Engineering
Dynamic hierarchical compact clustering algorithm
CIARP'05 Proceedings of the 10th Iberoamerican Congress conference on Progress in Pattern Recognition, Image Analysis and Applications
Improving the dynamic hierarchical compact clustering algorithm by using feature selection
CIARP'10 Proceedings of the 15th Iberoamerican congress conference on Progress in pattern recognition, image analysis, computer vision, and applications
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In this paper, a speed-up version of the Dynamic Hierarchical Compact (DHC ) algorithm is presented. Our approach profits from the cluster hierarchy already built to reduce the number of calculated similarities. The experimental results on several benchmark text collections show that the proposed method is significantly faster than DHC while achieving approximately the same clustering quality.