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
Flexible intrinsic evaluation of hierarchical clustering for TDT
CIKM '03 Proceedings of the twelfth international conference on Information and knowledge management
Feature Selection for Unsupervised Learning
The Journal of Machine Learning Research
A comparison of extrinsic clustering evaluation metrics based on formal constraints
Information Retrieval
Local Feature Selection in Text Clustering
Advances in Neuro-Information Processing
A Speed-Up Hierarchical Compact Clustering Algorithm for Dynamic Document Collections
CIARP '09 Proceedings of the 14th Iberoamerican Conference on Pattern Recognition: Progress in Pattern Recognition, Image Analysis, Computer Vision, and Applications
Dynamic hierarchical algorithms for document clustering
Pattern Recognition Letters
Dynamic hierarchical compact clustering algorithm
CIARP'05 Proceedings of the 10th Iberoamerican Congress conference on Progress in Pattern Recognition, Image Analysis and Applications
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Feature selection has improved the performance of text clustering. In this paper, a local feature selection technique is incorporated in the dynamic hierarchical compact clustering algorithm to speed up the computation of similarities. We also present a quality measure to evaluate hierarchical clustering that considers the cost of finding the optimal cluster from the root. The experimental results on several benchmark text collections show that the proposed method is faster than the original algorithm while achieving approximately the same clustering quality.