ee.Clusterer.wekaKMeans

Cluster data using the k-means algorithm. Can use either the Euclidean distance (default) or the Manhattan distance. If the Manhattan distance is used, then centroids are computed as the component-wise median rather than mean. For more information see:

D. Arthur, S. Vassilvitskii: k-means++: the advantages of careful seeding. In: Proceedings of the eighteenth annual ACM-SIAM symposium on Discrete algorithms, 1027-1035, 2007.

Usage	Returns
ee.Clusterer.wekaKMeans(nClusters, init, canopies, maxCandidates, periodicPruning, minDensity, t1, t2, distanceFunction, maxIterations, preserveOrder, fast, seed)	Clusterer

Argument	Type	Details
nClusters	Integer	Number of clusters.
init	Integer, default: 0	Initialization method to use. 0 = random, 1 = k-means++, 2 = canopy, 3 = farthest first.
canopies	Boolean, default: false	Use canopies to reduce the number of distance calculations.
maxCandidates	Integer, default: 100	Maximum number of candidate canopies to retain in memory at any one time when using canopy clustering. T2 distance plus, data characteristics, will determine how many candidate canopies are formed before periodic and final pruning are performed, which might result in exceess memory consumption. This setting avoids large numbers of candidate canopies consuming memory.
periodicPruning	Integer, default: 10000	How often to prune low density canopies when using canopy clustering.
minDensity	Integer, default: 2	Minimum canopy density, when using canopy clustering, below which a canopy will be pruned during periodic pruning.
t1	Float, default: -1.5	The T1 distance to use when using canopy clustering. A value < 0 is taken as a positive multiplier for T2.
t2	Float, default: -1	The T2 distance to use when using canopy clustering. Values < 0 cause a heuristic based on attribute std. deviation to be used.
distanceFunction	String, default: "Euclidean"	Distance function to use. Options are: Euclidean and Manhattan.
maxIterations	Integer, default: null	Maximum number of iterations.
preserveOrder	Boolean, default: false	Preserve order of instances.
fast	Boolean, default: false	Enables faster distance calculations, using cut-off values. Disables the calculation/output of squared errors/distances.
seed	Integer, default: 10	The randomization seed.