K means question

clustering
kmeans
partition
Noteworthy_Content

asked 2015-08-14 13:18:51 -0600

mrzl
61 ●1 ●1 ●5

updated 2015-12-21 03:05:28 -0600

sturkmen

6772 ●3 ●48 ●79 https://github.com/stu...

Hey everybody,

I'm currently in the process of clustering a set of points into different sections. Though, the k means algorithm requires me to explicitly set the number of clusters to divide the set of points in. Is there an alternative algorithm, where I can pass in a set of points and a maximum distance, which divides point clusters from each other? What I would like to have returned is a list of clusters of yet unknown size, containing a sub-set of points.

I would be very thankful for any directions.

Thanks, Marcel

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struct Dist { bool operator()(const Point& a, const Point &b) { return sqrt((a.x-b.x)*(a.x-b.x) + (a.y-b.y)*(a.y-b.y)) < 15; } }; vector<Point> input = ...; vector<int> labels; partition(input, labels, Dist());

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