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. ?
Fast Global K-means (FGK-means) REF , for example, is an incremental approach of clustering that dynamically adds one cluster centre at a time through a deterministic global search procedure consisting of D executions of the k-means algorithm with different suitable initial positions.
The global k-means clustering algorithm
Pattern Recognition
Computer Science