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Consider the following set of five 2D data points, which we seek to cluster hierarchically. | Consider the following set of five 2D data points, which we seek to cluster hierarchically. | ||
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[[Image:Lecture23ClustersRaw_OldKiwi.jpg]] | [[Image:Lecture23ClustersRaw_OldKiwi.jpg]] | ||
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| + | We may visualize the hierarchical clustering in various ways. One is by a Venn diagram, in which we circle the data points which belong to a cluster, then subsequently circle any clusters that belong to a larger cluster in the hierarchy. | ||
[[Image:Lecture23VennClusters_OldKiwi.jpg]] | [[Image:Lecture23VennClusters_OldKiwi.jpg]] | ||
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| + | Another way is to use a dendogram. A dendogram represents the clustering as a tree, with clusters that are more closely grouped indicated as siblings "earlier" in the tree. The dendogram also includes a "similarity scale," which indicates the distance between the data points (clusters) within a cluster. For the example dataset above (with distances calculated as Euclidian distance), we have the following dendogram: | ||
[[Image:Lecture23DendogramCluster_OldKiwi.jpg]] | [[Image:Lecture23DendogramCluster_OldKiwi.jpg]] | ||
Revision as of 10:49, 10 April 2008
Consider the following set of five 2D data points, which we seek to cluster hierarchically.
We may visualize the hierarchical clustering in various ways. One is by a Venn diagram, in which we circle the data points which belong to a cluster, then subsequently circle any clusters that belong to a larger cluster in the hierarchy.
Another way is to use a dendogram. A dendogram represents the clustering as a tree, with clusters that are more closely grouped indicated as siblings "earlier" in the tree. The dendogram also includes a "similarity scale," which indicates the distance between the data points (clusters) within a cluster. For the example dataset above (with distances calculated as Euclidian distance), we have the following dendogram:
