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
ACM Computing Surveys (CSUR)
Evaluation of hierarchical clustering algorithms for document datasets
Proceedings of the eleventh international conference on Information and knowledge management
Mean Shift Based Clustering in High Dimensions: A Texture Classification Example
ICCV '03 Proceedings of the Ninth IEEE International Conference on Computer Vision - Volume 2
Flexible intrinsic evaluation of hierarchical clustering for TDT
CIKM '03 Proceedings of the twelfth international conference on Information and knowledge management
Clustering, dimensionality reduction, and side information
Clustering, dimensionality reduction, and side information
Pattern Recognition
MultiK-MHKS: A Novel Multiple Kernel Learning Algorithm
IEEE Transactions on Pattern Analysis and Machine Intelligence
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This article provides a simple and general way for defining the recovery rate of clustering algorithms using a given family of old clusters for evaluating the performance of the algorithm when calculating a family of new clusters. Under the assumption of dealing with simulated data (i.e., known old clusters), the recovery rate is calculated using one proposed exact (but slow) algorithm, or one proposed approximate algorithm (with feasible run time).