# Akaike Information criterion for k-means

I am trying use the AIC & BIC for selecting the number of clusters for k-means. I found a post on Stackoverflow where some R code is posted, but I could not find any valid explanation for its correctness.

The code:

kmeansAIC = function(fit){
m = ncol(fit$centers) n = length(fit$cluster)
k = nrow(fit$centers) D = fit$tot.withinss
return(D + 2*m*k)
}


basically states

$$AIC = -2 \mathcal{L}(\hat{\theta}) + 2p = (\sum_{l=1}^k\sum_{i=1}^{n_l}(\vec{x_{i,l}}-\vec{\mu_l})^2)+2 d k$$ where $k$ is the number of clusters, $n_l$ is the number of point in cluster $l$, $\mu_l$ is the cluster center of cluster $l$ and $d$ is the dimensionality of each point.

Now my problem is with the derivation of the log-likelihood function. If I consider k-means as a form of an EM algorithm with spherical clusters I get a gaussian cluster $cl_i$ with density $f_{cl_i}$ like

$$f_{cl_i}(\vec{x}, \vec{\mu_i}, \mathbf{\Sigma}) = \frac{1}{\sqrt{(2 \pi)^d det(\mathbf{\Sigma}})} \exp(-\frac{1}{2} (\vec{x}-\vec{\mu})^T \mathbf{\Sigma}^{-1} (\vec{x}-\vec{\mu}))$$ with $\bf{\Sigma}$ as covariance of the cluster $cl_i$.

As the cluster is spherical, $\bf{\Sigma}$ can be written as $\bf{\Sigma}=\sigma^2 \bf{I}$ where $\bf{I}$ is the identiy matrix. This gives $f_{cl_i}$ as $$f_{cl_i}(\vec{x}, \vec{\mu_i}, \Sigma) = \frac{1}{\sqrt{(2 \pi \sigma^2)^d}} \exp(-\frac{(\vec{x}-\vec{\mu})^2}{2 \sigma^2})$$ and therefore a log-likelihood function $\mathcal{L}$ for a cluster \begin{aligned} \mathcal{L}(\Theta;\vec{x}) = \sum_{i=1}^{n} log( \frac{1}{\sqrt{(2 \pi \sigma^2)^d}} \exp(-\frac{(\vec{x_i}-\vec{\mu})^2}{2 \sigma^2}) ) = \\ - n \log( \sqrt{(2 \pi \sigma^2)^d}) - \frac{1}{2 \sigma^2} \sum_{i=1}^{n} (\vec{x_i}-\vec{\mu})^2 \end{aligned}

I understand, that the first term can be omitted, as I am only interested in differences of the AIC for different $k$ and that term is constant over $k$.

This leads me to the following formula for the AIC: $$AIC = \frac{1}{\sigma^2} (\sum_{l=1}^k\sum_{i=1}^{n_l}(\vec{x_{i,l}}-\vec{\mu_l})^2) + 2 k d$$

which differs in the factor $\frac{1}{\sigma^2}$ compared to the formula from Stackoverflow.

I know, that $\sigma^2$ does not matter for k-means itself but it is obvious, that a greater $\sigma^2$ penalizes the model complexity in the AIC. Thus it seems strange to me, to just assume $\sigma^2 = 1$.

Is there an error in my line of argumentation or am I missing something?

I also used the same concept for calculation of the Bayesian Information Criterion and observed, that I end up with a suspiciously large number of clusters if I select the minimum of the BIC. The BIC drops very fast in the beginning and then decreases very slowly.

Is it common to select an AIC/BIC value where the scoring function doesn't show big decreases anymore (similar to the elbow technique) or should it always be the minimum of the function?

• I'm confused. Isn't the $\sigma$ dependent on the cluster? In other words, should they be $\sigma_i$? – FinanceGuyThatCantCode Aug 1 '19 at 15:32

I think that the answer you refer to in the link in Stackoverflow, is in turn based on another link: http://sherrytowers.com/2013/10/24/k-means-clustering/.

In that post the data used is standardized the variance is equal to 1 and the formula makes sense. In any case I think your line of reasoning is absolutely correct, and one should refrain to use that expression if the underlying assumption is not correct.

I know this is an old (and answered) question, but I wanted to jump in because I ran into this same question, and was surprised by how little information there was available.

Before diving in to my answer, I wanted to echo what was said by FinanceGuyThatCantCode: you have a small error in your proposal. The $$\sigma$$ in your question really should be $$\sigma_i$$, since it depends on each cluster. So what you're asking is not why total variance is assumed equal to 1, but rather why each cluster variance is assumed equal to 1.

The answer is that the $$\sigma_i=1$$ assumption is actually an assumption of the k-means algorithm. Lets extend your analogy that k-means is really a latent Gaussian problem, now look at the k-means loss function: $$\min_{\mu_i} \sum_i \sum_{x_i \in S_i} |x_i - \mu_i|^2$$
Where $$S_i$$ is the set of $$x_i$$ assigned to center $$\mu_i$$. If we imagine that this loss function is the negative log-likelihood, and that what we're really doing is a likelihood maximization of the latent Gaussian means, we'd get the following equivalent likelihood maximization: $$\max_{\mu_i} \prod_i \prod_{x_i \in S_i} \exp \left[ (x_i - \mu_i)^2 \right]$$
Playing a little fast and loose with scaling constants, this is the same as: $$\max_{\mu_i} \prod_i \prod_{x_i \in S_i} N(x_i;\mu_i,\sigma_i^2 = 1)$$
Where $$N(x_i;\mu_i,\sigma_i^2 = 1)$$ is the normal PDf.

So we see that k-means implicitly assumes that the latent Gaussians have unit variance. This is because k-means isn't really meant to be a probabilistic model of the data; it's not a "generative" model which is intended to predict future datapoints. It's just an algorithm which uses a reasonable heuristic to produce label assignments.

Speculation incoming: I believe that if you wanted to relax the assumption that $$\sigma_i=1$$, then your best estimate would probably be something like $$\frac{1}{|S_i|-1} \sum_{x_i \in S_i} |x_i-\mu_i|^2$$, and so the AIC you propose (with the correction of $$\sigma$$ to $$\sigma_i$$) would collapse into something like: $$\sum_i |S_i| + 2 k d = n + 2 k d$$
Which is just a direct penalty on $$k$$.