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**Affinity Matrix**

An

**Affinity Matrix,**also called a*Similarity Matrix.*It is an essential statistical technique used to organize the mutual similarities between a set of data points.

These similarity measures can be interpreted as the probability that that two points are related.

For example, if two data points have close coordinates, then their cosine similarity score or respective “affinity” score will be much closer to 1 than two data points with a lot of space between them.

Typical examples of similarity measures are the

and the*Cosine Similarity**Jaccard Similarity.*This Affinity Matrix allows machine learning systems to work with data from unlabeled or corrupted datasets with human-like intuition, which leads to countless practical applications in many fields.

## Applications of an Affinity Matrix:

*1] Advanced Genetic Research –*

- Discovering and studying affinities among ostensibly random DNA combinations has delivered huge breakthroughs in medical science and pharmaceutical research.

*2] Efficient Data Mining –*

It allows fast and accurate recognition of hidden relationship patterns in any database full of unlabeled information.

Used in business intelligence, law enforcement, and all forms of scientific research.

*3] Intelligent Unsupervised Machine Learning –*

Implementing machine learning algorithms capable of deriving structure and meaning from “raw” unorganized data would simply be impossible without a similarity matrix to ensure a minimum standard of accuracy in what the network is teaching itself.

Machine learning algorithms like

and*k-means*depend on a choice of either a distance function or an affinity measure.*k-nearest neighbors*

*4] Smart Information Retrieval –*

- It acts as a driving force behind smart search engines that pull additional relevant information for you that you didn’t even know you needed.