A statistical model of cluster stability
Pattern Recognition
Automatically finding clusters in normalized cuts
Pattern Recognition
Probabilistic auto-tuning for architectures with complex constraints
Proceedings of the 1st International Workshop on Adaptive Self-Tuning Computing Systems for the Exaflop Era
A multivariate uniformity test for the case of unknown support
Statistics and Computing
Data clustering: a user’s dilemma
PReMI'05 Proceedings of the First international conference on Pattern Recognition and Machine Intelligence
Statistical measures of two dimensional point set uniformity
Computational Statistics & Data Analysis
An empirical study of tests for uniformity in multidimensional data
Computational Statistics & Data Analysis
How Many Clusters: A Validation Index for Arbitrary-Shaped Clusters
IEEE/ACM Transactions on Computational Biology and Bioinformatics (TCBB)
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Testing for uniformity of multivariate data is the initial step in exploratory pattern analysis. We propose a new uniformity testing method, which first computes the maximum (standardized) edge length in the MST of the given data. Large lengths indicate the existence of well-separated clusters or outliers in the data. For the data passing this edge inconsistency test, we generate two sub-samples of the data by a weighted resampling method, where the weights are computed based on the normalized edge lengths of MST of the entire data. The uniformity of the data is estimated by running the two-sample MST-test on these two subsamples. Experiments with simulated and real data show the potential of the proposed test in identifying uniform or weakly clustered data. This test can also be used to rank various data sets based on their degree of uniformity.