How many clusters are best?—an experiment
Pattern Recognition
Clustering Categorical Data: An Approach Based on Dynamical Systems
VLDB '98 Proceedings of the 24rd International Conference on Very Large Data Bases
WaveCluster: a wavelet-based clustering approach for spatial data in very large databases
The VLDB Journal — The International Journal on Very Large Data Bases
A Framework for Experimental Evaluation of Clustering Techniques
IWPC '00 Proceedings of the 8th International Workshop on Program Comprehension
Efficient and Effective Clustering Methods for Spatial Data Mining
Efficient and Effective Clustering Methods for Spatial Data Mining
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We highlight a partition clustering method, which proposes an experimental solution to the famous problem of automatic discovery of the number of clusters (k). The majority of partition clustering methods consider the manual valuation of k. Manual valuation of k may be interesting for specific domains of applications where the expert has an accurate idea of the number of clusters he wants, however it is unrealistic for generic applications, and needs important estimation efforts without any insurance of their efficiencies.