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Explain Affinity Matrix.
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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 Cosine Similarity and the 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 k-means and k-nearest neighbors depend on a choice of either a distance function or an affinity measure.

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.
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