3D平面中的聚类点

Question

Suppose we have 1000 random data points in a cube (as shown in the following image). The distribution of points in X and Y directions are uniform but not in Z direction. As we get deeper, the data points are denser. Is there any straightforward way in python to cluster these data points such that:

• each cluster has equal size
• each cluster consists of local points, ie, each cluster consists of points being close to each other.

I have already tried K-means clustering from Scipy package but it did not give me a good result and the points of each cluster were very widespread rather than being concentrated.

Answer 1

Try using Scikit-Learn's implementation. They initialize their clusters using a technique known as "K-Means++" which picks the first means probabilistically to get an optimal starting distribution. This creates a higher probability of a good result.

http://scikit-learn.org/stable/modules/generated/sklearn.cluster.KMeans.html