# Tag Info

Accepted

### Can KL-Divergence ever be greater than 1?

The Kullback-Leibler divergence is unbounded. Indeed, since there is no lower bound on the $q(i)$'s, there is no upper bound on the $p(i)/q(i)$'s. For instance, the Kullback-Leibler divergence ...
• 93.4k

### Hierarchical clustering with mixed type data - what distance/similarity to use?

If you have stumbled upon this question and are wondering what package to download for using Gower metric in R, the cluster package has a function named daisy(), ...
• 7,913
Accepted

### Intuition of the Bhattacharya Coefficient and the Bhattacharya distance?

The Bhattacharyya coefficient is $$BC(h,g)= \int \sqrt{h(x) g(x)}\; dx$$ in the continuous case. There is a good wikipedia article https://en.wikipedia.org/wiki/Bhattacharyya_distance. How to ...
• 66.4k
Accepted

### If Manhattan distance always performs better on a dataset...what does it mean?

Also use the search terms l1 norm, l1 distance, absolute deviance etc all of which refer to the same thing as manhattan distance. The properties of the l1-norm (manhattan distance) can largely be ...
• 2,167

### Coupling and Total variational distance

There are at least 2 ways to compute the total variation distance. The first is by using the definition of Total variation distance: $TV(\mu,\nu)=\sup_{ A\in \mathcal{F}}\left|\mu(A)-\nu(A)\right|,$ ...
• 3,969
Accepted

### Jaccard similarity coefficient vs. Point-wise mutual information coefficient

These two are quite different. Still, let us try to "bring them to a common denominator", to see the difference. Both Jaccard and PMI could be extended to a continuous data case, but we'll observe the ...
• 52.9k

### Why does k-means clustering algorithm use only Euclidean distance metric?

I might be a little pedantic here, but K-means is the name given to a particular algorithm that assigns labels to data points such that within cluster variances are minimized, and it is not the name ...
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