Loevinger's measures of rule quality for assessing cluster stability
Computational Statistics & Data Analysis
Data clustering: 50 years beyond K-means
Pattern Recognition Letters
A multivariate uniformity test for the case of unknown support
Statistics and Computing
Measuring the complexity of a collection of documents
ECIR'06 Proceedings of the 28th European conference on Advances in Information Retrieval
A test for spatial randomness based on k-NN distances
Pattern Recognition Letters
An empirical study of tests for uniformity in multidimensional data
Computational Statistics & Data Analysis
Spatial pattern recognition of seismic events in South West Colombia
Computers & Geosciences
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 in multidimensional data is important in exploratory pattern analysis, statistical pattern recognition, and image processing. The goal of this paper is to determine whether the data follow the uniform distribution over some compact convex set in K-dimensional space, called the sampling window. We first provide a simple, computationally efficient method for generating a uniformly distributed sample over a set which approximates the convex hul of the data. We then test for uniformity by comparing this generated sample to the data by using Friedman-Rafsky's minimal spanning tree (MST) based test. Experiments with both simulated and real data indicate that this MST-based test is useful in deciding if data are uniform.