# Tag Info

### 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, ...
• 52.6k
Accepted

### How to select a clustering method? How to validate a cluster solution (to warrant the method choice)?

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" ...
• 52.6k

### How to interpret the dendrogram of a hierarchical cluster analysis

I had the same questions when I tried learning hierarchical clustering and I found the following pdf to be very very useful. http://www.econ.upf.edu/~michael/stanford/maeb7.pdf Even if Richard is ...
• 391
Accepted

### Using correlation as distance metric (for hierarchical clustering)

Requirements for hierarchical clustering Hierarchical clustering can be used with arbitrary similarity and dissimilarity measures. (Most tools expect a dissimilarity, but will allow negative values - ...

### How to understand the drawbacks of Hierarchical Clustering?

Whereas $k$-means tries to optimize a global goal (variance of the clusters) and achieves a local optimum, agglomerative hierarchical clustering aims at finding the best step at each cluster fusion (...
• 3,928
Accepted

### Do I need to remove duplicate objects for cluster analysis of objects?

It changes the results. With k-means this should be straightforward to see: the mean of 0, 0 and 1 is different from 0 and 1. Usually this will also be the case for hierarchical clustering, but it ...
• 6,959

### Clustering -- Intuition behind Kleinberg's Impossibility Theorem

One way or another, every clustering algorithm relies on some notion of “proximity” of points. It seems intuitively clear that you can either use a relative (scale-invariant) notion or an absolute (...

• 51
Accepted

### Mahalanobis distance in a hierarchical cluster analysis in SPSS

IBM advises against using the Mahalanobis' distance in clustering. See here. In hierarchical clustering, you need to define the distance between the clusters (as they are formed) and the remaining ...
• 13.6k

### Can sub-optimality of various hierarchical clustering methods be assessed or ranked?

Only single-linkage is optimal. Complete-linkage fails, so your intuition was wrong there. ;-) As a simple example, consider the one-dimensional data set 1,2,3,4,5,6. The (unique) optimum solution ...
• 2,818

### Which unsupervised classification method to use next if hierarchical clustering gave bad results?

I loaded your data into R and applied hierarchical clustering with Ward's method, which gave 3 clean cut clusters for your stations (Fig.1). Then I applied Principal Component Analysis on the scaled ...
• 2,447

### Choosing the right linkage method for hierarchical clustering

The correlation between the distance matrix and the cophenetic distance is one metric to help assess which clustering linkage to select. From ?cophenetic: It can ...
• 230

### Is there any situation where PCA performs better than SVD?

PCA and SVD are not comparable. In short, SVD is a technique that one can use to compute the principal components in a PCA. It is possible to find the principal components without using SVD by ...
• 368
Accepted

### Motivation for Ward's definition of error sum of squares (ESS)

\begin{align} \operatorname{Var}(\vec x) \propto \sum_{i=1}^n(x_i - \bar x)^2 &= \sum_i x_i^2 - 2\bar x \sum_ix_i + n \bar x^2 \\ &= \sum_i x_i^2 - n \bar x^2 = \sum_i x_i^2 - \frac 1n \left(\...
• 18.7k

### Does k-means have any advantages over HDBSCAN expect for runtime?

Randomization can be valuable. You can run k-means several times to get different possible clusters, as not all may be good. With HDBSCAN, you will always get the same result again. Classifier: k-...
Accepted

### Best practices in the selection of distance metric and clustering methods for gene expression data

This will probably not be the answer you want or expect, but this is how I see these things. Clustering problem Clustering, to a degree, is almost always a subjective procedure. You decide how you ...
• 4,387