Question: In the field of Data Mining, is there a specific sub-discipline called 'Similarity'? If yes, what does it deal with. Any examples, links, references will be helpful.

Also, being new to the field, I would like the community opinion on how closely related Data Mining and Artificial Intelligence are. Are they synonyms, is one the subset of the other?

Thanks in advance for sharing your knowledge.

Answer: 
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> In the field of Data Mining, is there a specific sub-discipline called 'Similarity'? 
> 
> 
> 

Yes. There is a specific subfield in data mining and machine learning called metric learning, which aims to learn a better distance metric among data instances. 

Do you know any of the following concepts?

[Euclidean distance](http://en.wikipedia.org/wiki/Euclidean_distance)

[Mahalanobis distance](http://en.wikipedia.org/wiki/Mahalanobis_distance)

[Pearson correlation](http://en.wikipedia.org/wiki/Correlation_and_dependence)

[Cosine similarity](https://stackoverflow.com/questions/2859970/about-cosine-similarity) and [here](https://stackoverflow.com/questions/520241/cosine-similarity-of-vectors)

Kernel functions 

After you know these, you will know what is 'similarity'. 

> 
> I would like the community opinion on how closely related Data Mining and Artificial Intelligence are.
> 
> 
> 

It is very hard to distinguish what is data mining, what is AI. Don't discuss this question when you are new in the field. When you have learned 10 algorithms in data mining and read some AI books, you will know the difference and the relation.
Just to stress the importance of the "similarity" concept. 

Data mining (AI, machine learning, modelling etc) is about bringing some function to either it's maximum or minimum value. Take the best optimization/learning/mining algorithm and a wrong function and you get a complete garbage. Note that we use "value" and not "valueS". That's because there is no (to my best knowledge) algorithm (computational or other) that is capable of optimizing more than one value. However, in our Universe, complex optimizations are more frequent than one-dimensional ones (we want to be rich AND young AND healthy). That is why there a plethora of similarity and other scoring functions exists. And that is why none of them is "the right one"