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Simple k mean visualzation with d3 example script type text javascript src https d3js org d3 v7 min js script div id kmeans div style centroid stroke 000 stroke width 2px label font size 12px fill 333 axis line axis path fill none stroke width 1px stroke ccc shape rendering crispEdges style script document body style height 600px var elt kmeans var w 500 var h 500 var numPoints 1000 var numClusters 5 var maxIter 10 the current iteration var iter 1 var centroids var points var margin top 30 right 20 bottom 20 left 30 var width w margin left margin right var height h margin top margin bottom var colors d3 scaleOrdinal d3 schemeCategory10 var colors d3 scale category20 range var svg d3 select elt append svg style width width margin left margin right style height height margin top margin bottom var group svg append g attr transform translate margin left margin top svg append g append text attr class label attr transform translate width margin left margin right height margin top margin bottom text Computes the euclidian distance between two points function getEuclidianDistance a b var dx b x a x dy b y a y return Math sqrt Math pow dx 2 Math pow dy 2 Returns a point with the specified type and fill color and with random x y coordinates function getRandomPoint type fill return x Math round Math random width y Math round Math random height type type fill fill Generates a specified number of random points of the specified type function initializePoints num type var result for var i 0 i num i var color colors i if type centroid color ccc var point getRandomPoint type color point id point type i result push point return result Find the centroid that is closest to the specified point function findClosestCentroid point var closest i 1 distance width 2 centroids forEach function d i var distance getEuclidianDistance d point Only update when the centroid is closer if distance closest distance closest i i closest distance distance return centroids closest i All points assume the color of the closest centroid function colorizePoints points forEach function d var closest findClosestCentroid d d fill closest fill Computes the center of the cluster by taking the mean of the x and y coordinates function computeClusterCenter cluster return d3 mean cluster function d return d x d3 mean cluster function d return d y Moves the centroids to the center of their cluster function moveCentroids centroids forEach function d Get clusters based on their fill color var cluster points filter function e return e fill d fill Compute the cluster centers var center computeClusterCenter cluster Move the centroid d x center 0 d y center 1 Updates the chart function update var data points concat centroids The data join var circle group selectAll circle data data Create new elements as needed circle enter append circle attr id function d return d id attr class function d return d type attr r 5 Update old elements as needed circle group selectAll circle circle attr cx function d return d x attr cy function d return d y style fill function d return d fill Remove old nodes circle exit remove Updates the text in the label function setText text svg selectAll label text text Executes one iteration of the algorithm Fill the points with the color of the closest centroid this makes it part of its cluster Move the centroids to the center of their cluster function iterate Update label setText Iteration iter of maxIter Colorize the points colorizePoints Move the centroids moveCentroids Update the chart update The main function initializes the algorithm and calls an iteration every two seconds function initialize Initialize random points and centroids centroids initializePoints numClusters centroid points initializePoints numPoints point initial drawing update iterate iter var interval setInterval function if iter maxIter 1 iterate iter else clearInterval interval setText Done 1000 Call the main function initialize console log ready script

closest i All points assume the color of the closest centroid function colorizePoints points forEach function d var closest findClosestCentroid d d fill closest fill Computes the center of the cluster by taking the mean of the x and y coordinates function computeClusterCenter cluster return d3 mean cluster function d return d x d3 mean cluster function d return d y Moves the centroids to the center of their cluster function moveCentroids centroids forEach function d Get clusters based on their fill color var cluster points filter function e return e fill d fill Compute the cluster centers var center computeClusterCenter cluster Move the centroid d x center 0 d y center 1 Updates the chart function update var data points concat centroids The data join var circle group selectAll circle data data Create new elements as needed circle enter append circle attr id function d return d id attr class function d return d type attr r 5 Update old elements as needed circle group selectAll circle circle attr cx function d return d x attr cy function d return d y style fill function d return d fill Remove old nodes circle exit remove Updates the text in the label function setText text svg selectAll label text text Executes one iteration of the algorithm Fill the points with the color of the closest centroid this makes it part of its cluster Move the centroids to the center of their cluster function iterate Update label setText Iteration iter of maxIter Colorize the points colorizePoints Move the centroids moveCentroids Update the chart update The main function initializes the algorithm and calls an iteration every two seconds function initialize Initialize random points and centroids centroids initializePoints numClusters centroid points initializePoints numPoints point initial drawing update iterate iter var interval setInterval function if iter maxIter 1 iterate iter else clearInterval interval setText Done 1000 Call the main function initialize console log ready script

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