This link has been bookmarked by 25 people . It was first bookmarked on 10 Mar 2006, by yc c.
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20 Aug 14
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04 May 14
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a hierarchy of clusters
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The results of hierarchical clustering are usually presented in a dendrogram.
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a measure of dissimilarity between sets of observations is required. In most methods of hierarchical clustering, this is achieved by use of an appropriate metric (a measure of distance between pairs of observations), and a linkage criterion which specifies the dissimilarity of sets as a function of the pairwise distances of observations in the sets.
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Cutting the tree at a given height will give a partitioning clustering at a selected precision. I
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25 Dec 13
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- own cluster, and pairs of clusters are merged as one moves up the hierarchy.
- Divisive: This is a "top down" approach: all observations start in one cluste
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14 Dec 13
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In the general case, the complexity of agglomerative clustering is
, which makes them too slow for large data sets. Divisive clustering with an exhaustive search is 
, which is even worse. -
a linkage criterion which specifies the dissimilarity of sets as a function of the pairwise distances of observations in the sets.
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Hierarchical clustering has the distinct advantage that any valid measure of distance can be used. In fact, the observations themselves are not required: all that is used is a matrix of distances.
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08 Oct 13
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Agglomerative
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Divisive
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the complexity of agglomerative clustering is
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Maximum
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The linkage criterion determines the distance between sets of observations as a function of the pairwise distances between observations.
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Minimum
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single-linkage clustering
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average linkage clustering
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11 Jul 13
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Agglomerative: This is a "bottom up" approach: each observation starts in its own cluster, and pairs of clusters are merged as one moves up the hierarchy.
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Divisive: This is a "top down" approach: all observations start in one cluster, and splits are performed recursively as one moves down the hierarchy.
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13 Feb 12
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Euclidean distance
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Euclidean distance
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This is a "bottom up" approach:
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which clusters should be combined (for agglomerative), or where a cluster should be split (for divisive
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Euclidean distance
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Maximum or complete linkage clustering
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30 Nov 11
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23 Sep 11
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In statistics, hierarchical clustering is a method of cluster analysis which seeks to build a hierarchy of clusters.
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06 Apr 11
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- Agglomerative: This is a "bottom up" approach: each observation starts in its own cluster, and pairs of clusters are merged as one moves up the hierarchy.
- Divisive: This is a "top down" approach: all observations start in one cluster, and splits are performed recursively as one moves down the hierarchy.
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, which makes them too slow for large data sets. Divisive clustering with an exhaustive search is 
, which is even worse.
) are known: SLINK
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