Fitting straight lines to point patterns |
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Authors: | F. Murtagh A.E. Raftery |
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Affiliation: | Department of Computer Science, University College, Dublin, Republic of Ireland;Department of Statistics, Trinity College, Dublin 2, Republic of Ireland |
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Abstract: | In many types of point patterns, linear features are of greatest interest. A very general algorithm is presented here which determines non-overlapping clusters of points which have large linearity. Given a set of points, the algorithm successively merges pairs of clusters or of points, encompassing in the merging criterion both contiguity and linearity. The algorithm is a generalization of the widely-used Ward's minimum variance hierarchical clustering method. The application of this algorithm is illustrated using examples from the literature in biometrics and in character recognition. |
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Keywords: | Hierarchical clustering Constrained clustering Principal components analysis Karhunen-Loève expansion Variance Unsupervised classification |
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