Is there any way to compute Pearson's correlation between two strings I have two strings, e.g 


*

*str1="abddbabc" and 

*str2="bbcadbbd".


I know that each letter is representative of a floating point number, but I don't know what that number is. The only information that I have is that if a letter has higher alphabetical order its floating point value is larger (e.g floating point value of b is greater than a).
Is there anyway to compute Pearson's correlation (or any other association) between two string by knowing this information?
 A: If the only information you can glean from your strings is that they represent ranked lists (a < b < c etc), then I would suggest you replace the strings by their list of ranks (abddbabc -> [1, 2, 4, 4, 2, 1, 2, 3]) and use Spearman's correlation, or another rank correlation such as Kendall's tau.
Since you have a lot of repeated characters in your strings, take care about what you do with tied ranks.
A: This thread discusses some possible dependence metrics for non-numeric objects like text strings ... Random "words" game 
Specifically wrt your interest in identifying a Pearson-like metric for linear association between non-numeric text strings, the cosine similarity function is equivalent to the Pearson as a measure of linear dependence. Here's the wiki discussion of it ... https://en.wikipedia.org/wiki/Cosine_similarity
Personally, I don't agree with use of linear metrics, particularly wrt text mining. The nonlinear dependence metrics discussed in the thread above seem much more suitable to me. 
