Clustering and Search in Multi-Dimensional Spaces

KMeans2

KMeans2(samples, nclusters, labels, termcrit) → None

Splits set of vectors by a given number of clusters.

Parameters:

• samples (CvArr) – Floating-point matrix of input samples, one row per sample
• nclusters (int) – Number of clusters to split the set by
• labels (CvArr) – Output integer vector storing cluster indices for every sample
• termcrit (CvTermCriteria) – Specifies maximum number of iterations and/or accuracy (distance the centers can move by between subsequent iterations)

The function cvKMeans2 implements a k-means algorithm that finds the centers of nclusters clusters and groups the input samples around the clusters. On output, contains a cluster index for samples stored in the i-th row of the samples matrix.