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Results tagged with hierarchical-clustering
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user 247165
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.
5
votes
Question about Silhouette index calculation using scikit
(1) The distance function used should express as "correctly" as possible what distance "means" in the application in question. Any calculation based on distances, be it the clustering methods themselv …
- 28.3k
5
votes
Accepted
When doing hierarchical clustering, do we need to exclude variables with high correlation?
Ultimately the answer is "it depends". It depends on various things, including potential preprocessing and the distance you use (I guess Euclidean but be aware that this is not the only option). There …
- 28.3k
4
votes
K means clustering breakup---galaxy spectrum data set
In many real datasets (obviously I don't know about yours), clusters are not well separated, and even if they are, $k$-means clusters will not necessarily correspond to well separated subsets of the d …
- 28.3k
2
votes
When can I say there is no clusters?
Generally, as mentioned in the comments, there are different concepts of what a cluster is, and whether "there are clusters" or not depends on your cluster concept. I wrote something about this as Cha …
- 28.3k
2
votes
Accepted
Do $k$-means, dbscan, and hierarchical clustering all rely on (pseudo)metrics?
$k$-means in its standard form uses the Euclidean distance. This is necessary because otherwise the optimally cluster-representing centroids would not be means and the name $k$-means wouldn't be justi …
- 28.3k
1
vote
Hierarchical clustering and Dendrogram interpretation
The number of clusters problem is generally difficult and depends (as the problem of selecting a suitable clustering method) on the meaning of the data and the aim of clustering. Some methods produce …
- 28.3k
1
vote
Accepted
Clarification of algorithm for agglomerative hierarchical clustering
I think this just means that K is the target number of clusters where the procedure stops.
- 28.3k
0
votes
Accepted
(Agglomerative) Hierarchical Clustering: Which linkage for the detection of outliers?
Let's say an object is a singleton at high level in complete linkage, and say that there are otherwise bigger clusters. This means only that the maximum distances between the object and the other clus …
- 28.3k
0
votes
Accepted
From sets of elements to distance matrix
My first idea was what cdalitz wrote who can now make this an answer again. After the reply of Yanqi Huang, an alternative would be the Jaccard similarity: Divide the number sets in which both $a_i$ a …
- 28.3k
0
votes
Difference between Ward hierarchical clustering and K-Means for classification
(1) K-means and Ward's method can be seen as different approaches for optimising the same objective function (within cluster sum of squares, WSS). The performance of K-means regarding WSS depends on t …
- 28.3k