What is NMI in clustering?

Normalized mutual information (NMI) gives us the reduction in entropy of class labels when we are given the cluster labels. In a sense, NMI tells us how much the uncertainty about class labels decreases when we know the cluster labels. It is similar to the information gain in decision trees.

What is NMI in machine learning?

Normalized Mutual Information (NMI) is a normalization of the Mutual Information (MI) score to scale the results between 0 (no mutual information) and 1 (perfect correlation).

What is a good Mutual Information score?

The higher value, the closer connection between this feature and the target, which suggests that we should put this feature in the training dataset. If the MI score is 0 or very low like 0.01. the low score suggests a weak connection between this feature and the target.

Is Mutual Information normalized?

Normalized Mutual Information (NMI) is a measure used to evaluate network partitioning performed by community finding algorithms. It is often considered due to its comprehensive meaning and allowing the comparison of two partitions even when a different number of clusters (detailed below) [1].

How do you calculate entropy of a cluster?

The computation is straightforward. The probabilities are NumberOfMatches/NumberOfCandidates . The you apply base2 logarithms and take the sums. Usually, you will weight the clusters by their relative sizes.

Why correlation is better than mutual information?

Correlation analysis provides a quantitative means of measuring the strength of a linear relationship between two vectors of data. Mutual information is essentially the measure of how much “knowledge” one can gain of a certain variable by knowing the value of another variable.

How do you maximize mutual information?

Maximizing mutual information between features extracted from these views requires capturing information about high-level factors whose influence spans multiple views – e.g., presence of certain objects or occurrence of certain events.

What is PMI in NLP?

For statistics, probability theory and information theory, Pointwise mutual information (PMI), or point mutual information, is a measure of association. In contrast to mutual information (MI) which builds upon PMI, it refers to single events, whereas MI refers to the average of all possible events.

Which cluster has the smallest entropy?

The smallest possible value for entropy is 0.0, which occurs when all symbols in a vector are the same. In other words, there’s no disorder in the vector. The larger the value of entropy, the more disorder there is in the associated vector. And so the entropy for cluster k = 0 is 0.92 + 0.92 + 0.00 = 1.84.