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Results tagged with hierarchical-clustering
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Hierarchical cluster analysis is a method of cluster analysis which builds, by steps, a hierarchy of clusters, a dendrogram. Most popular is agglomerative hierarchical clustering (HAC) which starts from individual objects and collects them into bigger and bigger clusters.
3
votes
Accepted
Validate Cluster Analysis by doing it on two subsamples
Yes, you can do it. This sort of cross-validation or staibility check aims against overfitting always catching one with a single sample. It is just one among possible strategies. (I'm not in agreement …
- 58.8k
1
vote
Accepted
Dendrogram in Hybrid Hierarchical Clustering and Cut-off criterion (Calinski-Harabasz presen...
The clustering itself is done using the Euclidean Distance - however
the dendrogram is depicted using the squared Euclidean Distance. They
don't explain ...
From the looks of the dendrogram o …
- 58.8k
2
votes
Am I right that Calinski-Harabasz index (Pseudo-F) can not be calculated from a distance mat...
Calinski-Harabasz criterion and similar clustering indices based on ANOVA terms SSbetween, SSwithin, SStotal, can still be computed from the distance matrix between the objects, albeit computing them …
- 58.8k
2
votes
Accepted
Using Ward's method on a dissimilarity matrix of Gower distances
Ward's linkage method (it is not a "variance" method, - it is the "increase of sum-of-squares" method) requires (squared) euclidean distances. See also.
Gower distance sqrt(1-GS) is geometrically "eu …
- 58.8k
3
votes
Compare two hierarchical trees
A similar question partly concerns with it. In short, compute correlation coefficient between the following two variables (and cases are all possible pairs of objects): (V1) how much close the two obj …
- 58.8k
1
vote
Accepted
Hierarchical clustering in R - centroid linkage - problem with dendrogram heights
Explaining calculations done in centroid linkage hierarchical clustering
Your data: 5 points in 1D feature space:
a 1
b 2
c 6
d 11
e 16
Compute squared euclidean distances (because cent …
- 58.8k
3
votes
How to assign existing cluster numbers for future data, using hierarchical clustering algori...
My answer is about agglomerative (i.e. bottom-up) hierarchical cluster analysis, HAC, which methods are overviewed here. (I'm not quite sure if the answer can be extended to divisive, top-down hierarc …
- 58.8k
2
votes
Accepted
Cluster analysis with interchangeable, binary classes
Your task is the comparison of partitions without knowing any labels for the groups. This task is also called "assessment of agreement between clusterings" (contrasted to "assessment of agreement betw …
- 58.8k
7
votes
Accepted
Does a distance have to be a "metric" for an hierarchical clustering to be valid on it?
Requirements for distances depend on method of hierarchical clustering. Single, complete, average methods need distances to be no-negative and symmetric. Ward, centroid, median methods need (squared) …
- 58.8k
97
votes
Choosing the right linkage method for hierarchical clustering
Methods overview
Short reference about some linkage methods of hierarchical agglomerative cluster analysis (HAC).
Basic version of HAC algorithm is one generic; it amounts to updating, at each step, b …
- 58.8k
77
votes
Accepted
How to select a clustering method? How to validate a cluster solution (to warrant the method...
Often they say that there is no other analytical technique as strongly of the "as you sow you shall mow" kind, as cluster analysis is.
I can imagine of a number dimensions or aspects of "rightness" of …
- 58.8k