D2 Distance Metric: Learn More & Find Resources

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The discussion centers on the d2 distance metric, specifically the Euclidean L2 metric, which measures similarity in multi-dimensional space using the formula d((x1,y1), (x2,y2)) = √((x2-x1)² + (y2-y1)²). It is highlighted as a key metric in computing for assessing similarity between data points. Alternatives to the Euclidean distance include the Pearson correlation coefficient, the Jaccard coefficient, and the Manhattan distance. The original poster seeks resources or databases that list academic papers on this topic, indicating a need for more structured information on distance metrics in computing.
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i'd like to know more about the d2 distance metric. I'm assuming it's a topic in computing, however, i don't know of a database that contains a listing of such papers. could anyone point me in the right direction?
 
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Do you mean the Euclidean L_2 metric
d((x_1,y_1), (x_2,y_2))=\sqrt{(x_2-x_1)^2+(y_2-y_1)^2}
 
The Euclidean distance metric in computing is used to determine how similar things are in a multi-dimensional space. Alternatively one could use the the Pearson correlation coefficient, the Jaccard coefficient or the Manhattan distance.
 
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