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June 11, 2005Interface / CSNA Meeting St. Louis1 Estimating the Cluster Tree of a Density Werner Stuetzle Rebecca Nugent Department of Statistics University.

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Presentation on theme: "June 11, 2005Interface / CSNA Meeting St. Louis1 Estimating the Cluster Tree of a Density Werner Stuetzle Rebecca Nugent Department of Statistics University."— Presentation transcript:

1 June 11, 2005Interface / CSNA Meeting St. Louis1 Estimating the Cluster Tree of a Density Werner Stuetzle Rebecca Nugent Department of Statistics University of Washington

2 June 11, 2005Interface / CSNA Meeting St. Louis2

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4 June 11, 2005Interface / CSNA Meeting St. Louis4 Groups K-means with k = 2

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6 June 11, 2005Interface / CSNA Meeting St. Louis6 Detect that there are 5 or 6 distinct groups. Assign group labels to observations.

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20 June 11, 2005Interface / CSNA Meeting St. Louis20 rs = 1 rs = 5 rs = 2

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22 June 11, 2005Interface / CSNA Meeting St. Louis22 Note large tree fragments in bottom left panel

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48 June 11, 2005Interface / CSNA Meeting St. Louis48 rs = 1 rs = 5 rs = 2

49 June 11, 2005Interface / CSNA Meeting St. Louis49 Runt Pruning algorithm: generate_cluster_tree_node (mst, runt_size_threshold) { node = new_cluster_tree_node node.leftson = node.rightson = NULL node.obs = leaves (mst) cut_edge = longest_edge_with_large_runt_size (mst, runt_size_threshold) if (cut_edge) { node.leftson = generate_cluster_tree_node (left_subtree (cut_edge), runt_size_threshold) node.rightson = generate_cluster_tree_node (right_subtree (cut_edge), runt_size_threshold) } return (node) } rs = 1 rs = 5 rs = 2

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51 June 11, 2005Interface / CSNA Meeting St. Louis51 Note large tree fragments in bottom left panel

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71 June 11, 2005Interface / CSNA Meeting St. Louis71 Runt Pruning algorithm: generate_cluster_tree_node (mst, runt_size_threshold) { node = new_cluster_tree_node node.leftson = node.rightson = NULL node.obs = leaves (mst) cut_edge = longest_edge_with_large_runt_size (mst, runt_size_threshold) if (cut_edge) { node.leftson = generate_cluster_tree_node (left_subtree (cut_edge), runt_size_threshold) node.rightson = generate_cluster_tree_node (right_subtree (cut_edge), runt_size_threshold) } return (node) } rs = 1 rs = 5 rs = 2

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