ClusterNumber (FSharp.Stats) | FSharp.Stats

FSharp.Stats

ClusterNumber Module

Namespace: FSharp.Stats.ML.Unsupervised
Assembly: FSharp.Stats.dll

Types

Type	Description
SilhouetteResult

Functions and values

Function or value	Description
`calcAIC bootstraps iClustering maxK` Full Usage: `calcAIC bootstraps iClustering maxK` Parameters: bootstraps : `int` - iClustering : `int -> KClusteringResult<float[]>` - maxK : `int` - Returns: `(int * float) array`	Akaike Information Criterion (AIC) bootstraps : `int` iClustering : `int -> KClusteringResult<float[]>` maxK : `int` Returns: `(int * float) array` Example
`calcNMI correctLabels clusteredLabels` Full Usage: `calcNMI correctLabels clusteredLabels` Parameters: correctLabels : `int[]` - True data labels represented by integers clusteredLabels : `int[]` - Cluster indices represented by integers Returns: `float` Returns a NMI between 0 and 1. With 1 being a perfect match.	Calculates Normalized Mutual Information as a measure for clustering quality The correctLabels and Clustered Labels must have the same length correctLabels : `int[]` True data labels represented by integers clusteredLabels : `int[]` Cluster indices represented by integers Returns: `float` Returns a NMI between 0 and 1. With 1 being a perfect match. Example `let trueLabels = [\|"blue";"blue";"yellow";"red";"yellow"\|] let trueLabelsAsInt = [\|1; 1; 3; 2; 3\|] let clusteredLabels = [\|6; 6; 5; 5; 5\|] let nmi = calcNMI trueLabelsAsInt clusteredLabels //results in 0.77897` val trueLabels: string array val trueLabelsAsInt: int array val clusteredLabels: int array val nmi: obj
`kRuleOfThumb observations` Full Usage: `kRuleOfThumb observations` Parameters: observations : `'a seq` - Returns: `float`	Simple estimator for number of cluster (k) // can be used as the upper bound for other methods observations : `'a seq` Returns: `float` Example
`silhouetteIndex clusteredData` Full Usage: `silhouetteIndex clusteredData` Parameters: clusteredData : `float[][][]` - Returns: `float`	Calculates the silhouette score for a clustered data set where the coordinates of each data point is given as float []. The index ranges from -1 (bad clustering result) to 1 (perfekt clustering result) clusteredData : `float[][][]` Returns: `float` Example
`silhouetteIndexKMeans bootstraps iClustering data maxK` Full Usage: `silhouetteIndexKMeans bootstraps iClustering data maxK` Parameters: bootstraps : `int` - iClustering : `int -> KClusteringResult<float[]>` - data : `float[][]` - maxK : `int` - Returns: `SilhouetteResult array`	The silhouette index can be used to determine the optimal cluster number in k means clustering. bootstraps indicates the number the k means clustering is performed for each k and maxK indicated the maximal cluster number. bootstraps : `int` iClustering : `int -> KClusteringResult<float[]>` data : `float[][]` maxK : `int` Returns: `SilhouetteResult array` Example