Competitive learning algorithms for vector quantization
Neural Networks
Pattern Recognition with Fuzzy Objective Function Algorithms
Pattern Recognition with Fuzzy Objective Function Algorithms
A BYY Split-and-Merge EM Algorithm for Gaussian Mixture Learning
ISNN '08 Proceedings of the 5th international symposium on Neural Networks: Advances in Neural Networks
(Automatic) Cluster Count Extraction from Unlabeled Data Sets
FSKD '08 Proceedings of the 2008 Fifth International Conference on Fuzzy Systems and Knowledge Discovery - Volume 01
Automatically Determining the Number of Clusters in Unlabeled Data Sets
IEEE Transactions on Knowledge and Data Engineering
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Determining the number of clusters in a dataset has been one of the most challenging problems in clustering analysis. In this paper, we propose a stage by stage pruning algorithm to detect the cluster number for a dataset. The main idea is that from the dataset we can search for the representative points of clusters with the highest accumulation density and delete the other points from their neighborhoods stage by stage. As the radius of the neighborhood increases, the number of searched representative points decreases. However, the structure of actual clusters of the dataset makes the representative point number be stable at the true cluster number in a relative large interval of the radius, which helps us to detect the cluster number. It is demonstrated by the simulation and practical experiments that the proposed algorithm can lead to an effective estimate of the cluster number for a general dataset.