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=== Load Sample Data | |
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(_8:`Tweet` {`created_at`:"Tue Sep 16 07:49:19 +0000 2014", `favorites`:0, `id`:511783879238488064, `text`:"RT @selinasorrels: DON'T BE AFRAID~~~ BE AWARE...Computer Models Tell Us That This Ebola Pandemic Could Soon Kill Millions... #Geo-Eng~ | |
ht…"}), | |
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== Green Man POC | |
:neo4j-version: neo4j-2.1 | |
:author: John Swain | |
:twitter: @swainjo | |
:tags: domain:POC | |
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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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<!DOCTYPE html> | |
<html xmlns="http://www.w3.org/1999/xhtml"> | |
<head> | |
<meta charset="utf-8"> | |
<meta http-equiv="Content-Type" content="text/html; charset=utf-8" /> | |
<meta http-equiv="Content-Style-Type" content="text/css" /> | |
<meta name="generator" content="pandoc" /> |
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<!DOCTYPE html> | |
<meta charset="utf-8"> | |
<style> | |
body { | |
font-family: "Helvetica Neue", Helvetica, Arial, sans-serif; | |
width: 960px; | |
height: 500px; | |
position: relative; | |
} |