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K Mean

K-Means is one of the most popular "clustering" algorithms

K-means stores **k** centroids that it uses to define clusters. A point is considered to be in a particular cluster if it is closer to that cluster's centroid than any other centroid.

Grouping points into k (no.s) clusters

// Group by Centroid

def k1(pnts:var[]..[],cnts:var[]..[])

{

//Labels

lbl1 = "A"..#(List.Count(cnts))..1;

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//Distance to Centroids

dst1 = pnts<1>.DistanceTo(cnts<2>);

dst2 = List.SortByKey(lbl1,dst1<1>);

dst3 = List.FirstItem(dst2["sortedList"]<1>);

â€‹

//Group by Closest to Centroid

pnt1 = List.GroupByKey(pnts,dst3)["groups"];

return pnt1;

};

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// Centroid

def ctr (pnts:var[]..[])

{

return Point.ByCoordinates(Math.Average(pnts.X),

Math.Average(pnts.Y),Math.Average(pnts.Z));

};

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// K Mean

def kMean (pnts:var[]..[],k:int)

{

pnt1 = k1(pnts,pnts[1..k]);

cnt1 = 1;

pnt2 = [];

pts1 = [Imperative]

{

while (cnt1 <= k)

{

ctr1 = ctr(pnt1);

pnt2 = k1(List.Flatten(pnt1,-1),ctr1);

cnt1 = cnt1 + 1;

pnt1 = pnt2;

}

return pnt2;

}

return pts1;

};

Last modified 1yr ago

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