Home → Magazine Archive → July 2019 (Vol. 62, No. 7) → Good Algorithms Make Good Neighbors → Abstract

Good Algorithms Make Good Neighbors

By Erica Klarreich

Communications of the ACM, Vol. 62 No. 7, Pages 11-13

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A host of different tasks—such as identifying the song in a database most similar to your favorite song, or the drug most likely to interact with a given molecule—have the same basic problem at their core: finding the point in a dataset that is closest to a given point. This "nearest neighbor" problem shows up all over the place in machine learning, pattern recognition, and data analysis, as well as many other fields.

Yet the nearest neighbor problem is not really a single problem. Instead, it has as many different manifestations as there are different notions of what it means for data points to be similar. In recent decades, computer scientists have devised efficient nearest neighbor algorithms for a handful of different definitions of similarity: the ordinary Euclidean distance between points, and a few other distance measures.


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