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The k-nearest neighbors (k-NN) algorithm is among the simplest algorithms in the data mining field. Distances / similarities are calculated between each element in the data set using some distance / similarity metric ^[1]^ that the researcher chooses (there are many distance / similarity metrics), where the distance / similarity between any two elements is calculated based on the two elements' attributes. A data element’s k-NN are the k closest data elements according to this distance / similarity.
1. A distance metric measures distance; the higher the distance the further apart the neighbors. A similarity metric measures similarity; the higher the similarity the closer the neighbors.
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== Green Man POC | |
:neo4j-version: neo4j-2.1 | |
:author: John Swain | |
:twitter: @swainjo | |
:tags: domain:POC | |
=== Load Sample Data | |
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//hide |
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=== Load Sample Data | |
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[source,cypher] | |
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create | |
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