Documentation
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Index ¶
- func AgglomerativeClustering(points []clustering.Point, clusterDistAlgo clustering.DistanceMeasure, ...) (clusterlevels *[][]clustering.Cluster)
- func KMeans(k int, epsilon float64, points []clustering.Point, ...) (finalCluster []clustering.Cluster, err error)
- func Munkres(matrix [][]int) *[]int
- type LevelExtractor
Constants ¶
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Variables ¶
This section is empty.
Functions ¶
func AgglomerativeClustering ¶
func AgglomerativeClustering(points []clustering.Point, clusterDistAlgo clustering.DistanceMeasure, pointDistAlgo clustering.PointDistance) (clusterlevels *[][]clustering.Cluster)
Takes a set of n points and a distance measure and computes a hierarchical clustering containing n levels.
func KMeans ¶
func KMeans(k int, epsilon float64, points []clustering.Point, clusterDistAlgo clustering.DistanceMeasure, pointDistAlgo clustering.PointDistance) (finalCluster []clustering.Cluster, err error)
Produces a clustering of the given points into k clusters using the k-means clustering approach. The parameter epsilon is used to test for convergence of the cluster assignment step. @return []cluster the final clustering @return int the number of points considered in the clustering @return int64 the timestamp of the latest point taken into consideration
Types ¶
type LevelExtractor ¶
type LevelExtractor func(int, clustering.DistanceMeasure, clustering.PointDistance, ...*[][]clustering.Cluster) int
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