Introduction - If you have any usage issues, please Google them yourself
K-means clustering algorithm, which belongs to unsupervised machine learning algorithms for a given data set k clusters algorithm found.
First, k is randomly determined as a centroid of the initial point, and then assigning each data point to a cluster set, find a nearest point to each centroid,
It is assigned to the corresponding cluster centroids, update the centroids of each cluster until the centroid not changed.
K-means clustering algorithm k is an advantage of user-defined parameters, the user does not know whether it is good, at the same time, K-Means clustering algorithm converges but poor results,
Since the algorithm converges to a local minimum instead of the global minimum.
A variant K-means clustering algorithm is K-means clustering dichotomy algorithm will first of all points as a cluster, then the cluster into two,
After selecting one of the cluster continues to be divided, to choose which one cluster is divided depends on whether you can reduce the value of t