ysonggit/k-means-for-shape-sets

It is a C++ version of K-means Clustering. And the data set is Shape Set.

K-means-for-Shape-Sets

It is a C++ version of K-means Clustering. And the data set is Shape Set.

Data

The data set, R15, is a Shape Set, famous for Clustering. It contains 600 vectors, 2 dimensions and 15 clusters. It is first published in Veenman, C.J., M.J.T. Reinders, and E. Backer, A maximum variance cluster algorithm. Pattern Analysis and Machine Intelligence, IEEE Transactions on, 2002. 24(9): p. 1273-1280.

Here is an online copy.

K-means

K-means clustering is a method of vector quantization originally from signal processing, that is popular for cluster analysis in data mining. k-means clustering aims to partition n observations into k clusters in which each observation belongs to the cluster with the nearest mean, serving as a prototype of the cluster. This results in a partitioning of the data space into Voronoi cells.

Output

iteration N: the N-th round The N-th Class Mean Point: The number of points in N-th Class