Algorithm AS 136: A k-means clustering algorithm
JA Hartigan, MA Wong - Journal of the royal statistical society. series c …, 1979 - JSTOR
JA Hartigan, MA Wong
Journal of the royal statistical society. series c (applied statistics), 1979•JSTORMETHOD The algorithm requires as input a matrix of M points in N dimensions and a matrix
of K initial cluster centres in N dimensions. The number of points in cluster L is denoted by
NC (L). D (I, L) is the Euclidean distance between point I and cluster L. The general
procedure is to search for a K-partition with locally optimal within-cluster sum of squares by
moving points from one cluster to another.
of K initial cluster centres in N dimensions. The number of points in cluster L is denoted by
NC (L). D (I, L) is the Euclidean distance between point I and cluster L. The general
procedure is to search for a K-partition with locally optimal within-cluster sum of squares by
moving points from one cluster to another.
METHOD
The algorithm requires as input a matrix of M points in N dimensions and a matrix of K initial cluster centres in N dimensions. The number of points in cluster L is denoted by NC (L). D (I, L) is the Euclidean distance between point I and cluster L. The general procedure is to search for a K-partition with locally optimal within-cluster sum of squares by moving points from one cluster to another.
JSTOR