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

https://stanford.edu/~cpiech/cs221/handouts/kmeans.html

// Group by Centroid
def k1(pnts:var[]..[],cnts:var[]..[])
{
	//Labels
	lbl1 = "A"..#(List.Count(cnts))..1;

	//Distance to Centroids
	dst1 = pnts<1>.DistanceTo(cnts<2>);
	dst2 = List.SortByKey(lbl1,dst1<1>);
	dst3 = List.FirstItem(dst2["sortedList"]<1>);

	//Group by Closest to Centroid
	pnt1 = List.GroupByKey(pnts,dst3)["groups"];
	return pnt1;
};

// Centroid
def ctr (pnts:var[]..[])
{
	return Point.ByCoordinates(Math.Average(pnts.X),
	Math.Average(pnts.Y),Math.Average(pnts.Z));
};

// 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;
};