As per my previous post, I am working through Programming Collection Intelligence the first couple algorithms described in this book are regarding finding a similarity score, the methods they work through are Euclidean Distance and the Pearson Correlation Coefficient. The Manhattan distance score is also mentioned but some what I could find it seems that it is just the sum of the (absolute) differences of their coordinates, instead of Math.pow 2 used in Euclidean distance.
I worked through this and wrote/found some java equivalents for future use:
Pearson Correlation Coefficient:
Saturday, May 11, 2013
Similarity Score Algorithms
Labels: AI, Algorithms, Machine Learning
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