Hierarchical Clustering by Means of Model Grouping
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In many applications we are interested in finding clusters of data that share the same properties, like linear shape. We propose a hierarchical clustering procedure that merges groups if they are fitted well by the same linear model. The representative orthogonal model of each cluster is estimated robustly using iterated LQS regressions. We apply the method to two artificial datasets, providing a comparison of results against other non-hierarchical methods that can estimate linear clusters.
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