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I'm working on a document clustering application and decided to use Normalized Mutual Information as one of the measures of effectivenes. But I don't really understand how to implement this in that situation. In the the formula is transformed to (185), and in this publication (‎, page 8, formula 17) it looks slightly different, n(h,l) is not divided by total number of documents N. So, which formula is correct? I would be very grateful for possibly simple explantation.

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have you tried ? – cactus314 Jul 7 '13 at 13:06
yes, but no answer so far, so I thought to post it here as well. – user1315305 Jul 7 '13 at 13:12
up vote 1 down vote accepted

Each clustering algorithm $C=\{C_1,\dots,C_k\}$ defines the probability distribution $P_{\mathcal C}$

$$P_{\mathcal C}(i)=\frac{n_i}{N},$$

where $n_i$ is the number of points in the $i$-th cluster $C_i$ and $n$ is the total number of points in the data cloud. Different cluster algorithms can determine different numbers of clusters, of course.

For any distributions $P_{\mathcal C_1}=(p_1,\cdots,p_n)$ and $P_{\mathcal C_2}=(q_1,\cdots,q_m)$ the mutual information $I(p,q)$ is just

$$I(p,q)=\sum_{i,j}R(i,j)\log\frac{R(i,j)}{P_{\mathcal C_1}(i)P_{\mathcal C_2}(j)},$$

denoting by $R(i,j)$ the joint probability distribution. Using the definition for $P_{\mathcal C_i}$ and $R$, i.e.

$$P_{\mathcal C_1}(i)=\frac{n_i}{N} $$ $$P_{\mathcal C_2}(j)=\frac{m_j}{N} $$ $$R(i,j)=\frac{n_{i,j}}{N}:=\frac{|n_i\cap m_j|}{N}$$

we arrive at

$$I(P_{\mathcal C_1},P_{\mathcal C_2})=\sum_{i,j}\frac{n_{i,j}}{N}\log\frac{\frac{n_{i,j}}{N}}{\frac{n_i}{N}\frac{m_j}{N}}=\sum_{i,j}\frac{n_{i,j}}{N} \log\frac{N n_{i,j}}{n_i m_j},$$

as in, formula (185).

The same formula for the mutual information contained in and cited in does not contain the factor $\frac{1}{N}$, as you correctly remark.

I would use the above formulation for the mutual information, as it implements the correct probabilitstic view for the univariate and joint distributions.

Remark Note that in the assertion at pag. 589 "I(X,Y) is a metric" is wrong. The mutual information $I(X,Y)$ is equivalent to a Kullback Leibner divergence and it is no metric (or distance). The information value (or variation of information) is a metric, instead. Please look at for more details.

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Thank you, that makes sense now. I will do as you suggested. – user1315305 Jul 7 '13 at 13:58
I would use the above formulation for the mutual information But which? Both of the formulae are "above" – Sibbs Gambling Dec 23 '14 at 1:40
Formula 185 in… – Avitus Dec 23 '14 at 11:29

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