Random Start Forward Searches with Envelopes for Detecting Clusters in Multivariate Data
Conference paper
Abstract
During a forward search the plot of minimum Mahalanobis distances of observations not in the subset provides a test for outliers. However, if clusters are present in the data, their simple identification requires that there arc searches that initially include a preponderance of observations from each of the unknown clusters. We use random starts to provide such searches, combined with simulation envelopes for precise inference about clustering.
Keywords
Mahalanobis Distance Subset Size Random Start Forward Search Outlier Test
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References
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