We present the global k-means algorithm which is an incremental approach to clustering that dynamically adds one cluster center at a time through a deterministic global search procedure consisting of N (with N being the size of the data set) executions of the k-means algorithm from suitable initial positions. We also propose modiÿcations of the method to reduce the computational load without signiÿcantly a ecting solution quality. The proposed clustering methods are tested on well-known data sets and they compare favorably to the k-means algorithm with random restarts. ?
Likas et. al. REF proposed the Global k-means method for selecting one seed at a time to reduce final mean squared error.
The global k-means clustering algorithm
Pattern Recognition
Computer Science