The Silhouette Score is a metric of performance for clustering that represents the quality of clusters made as a result. More...

#include <silhouette_score.hpp>

Static Public Member Functions
static double	MeanDistanceFromCluster (const arma::colvec &distances, const arma::Row< size_t > &labels, const size_t &label, const bool &sameCluster=false)
	Find mean distance of element from a given cluster. More...

template<typename DataType , typename Metric >
static double	Overall (const DataType &X, const arma::Row< size_t > &labels, const Metric &metric)
	Find the overall silhouette score. More...

template<typename DataType >
static arma::rowvec	SamplesScore (const DataType &distances, const arma::Row< size_t > &labels)
	Find the individual silhouette scores for precomputted dissimilarites. More...

template<typename DataType , typename Metric >
static arma::rowvec	SamplesScore (const DataType &X, const arma::Row< size_t > &labels, const Metric &metric)
	Find silhouette score of all individual elements. More...

Static Public Attributes
static const bool	NeedsMinimization = false
	Information for hyper-parameter tuning code. More...

Detailed Description

The Silhouette Score is a metric of performance for clustering that represents the quality of clusters made as a result.

It provides an indication of goodness of fit and therefore a measure of how well unseen samples are likely to be predicted by the model, considering the inter-cluster and intra-cluster dissimilarities. Silhoutte Score is dependent on the metric used to calculate the dissimilarities. The best possible score is $ s(i) = 1.0 $ . Smaller values of Silhouette Score indicate poor clustering. Negative values would occur when a wrong label was put on the element. Values near zero indicate overlapping clusters. For an element i $ a(i) $ is within cluster average dissimilarity and $ b(i) $ is minimum of average dissimilarity from other clusters. the Silhouette Score $ s(i) $ of a Sample is calculated by

$\begin{eqnarray*} s(i) &=& \frac{b(i) - a(i)}{max\{b(i), a(i)\}} \end{eqnarray*}$

The Overall Silhouette Score is the mean of individual silhoutte scores.

Definition at line 40 of file silhouette_score.hpp.

Member Function Documentation

◆ MeanDistanceFromCluster()

static double MeanDistanceFromCluster	(	const arma::colvec &	distances,
		const arma::Row< size_t > &	labels,
		const size_t &	label,
		const bool &	sameCluster = `false`
	)

static

Find mean distance of element from a given cluster.

Parameters

distances	colvec containing distances from other elements.
labels	Labels assigned to data by clustering.
label	label of the target cluster.
sameCluster	true if calculating mean distance from same cluster.

Returns: (double) distance from the cluster.

◆ Overall()

static double Overall	(	const DataType &	X,
		const arma::Row< size_t > &	labels,
		const Metric &	metric
	)

static

Find the overall silhouette score.

Parameters

X	Column-major data used for clustering.
labels	Labels assigned to data by clustering.
metric	Metric to be used to calculate dissimilarity.

Returns: (double) silhouette score.

◆ SamplesScore() [1/2]

static arma::rowvec SamplesScore	(	const DataType &	distances,
		const arma::Row< size_t > &	labels
	)

static

Find the individual silhouette scores for precomputted dissimilarites.

Parameters

distances	Square matrix containing distances between data points.
labels	Labels assigned to data by clustering.

Returns: (arma::rowvec) element-wise silhouette score.

◆ SamplesScore() [2/2]

static arma::rowvec SamplesScore	(	const DataType &	X,
		const arma::Row< size_t > &	labels,
		const Metric &	metric
	)

static

Find silhouette score of all individual elements.

(Distance not precomputed).

Parameters

X	Column-major data used for clustering.
labels	Labels assigned to data by clustering.
metric	Metric to be used to calculate dissimilarity.

Returns: (arma::rowvec) element-wise silhouette score.

Member Data Documentation

◆ NeedsMinimization

const bool NeedsMinimization = false

static

Information for hyper-parameter tuning code.

It indicates that we want to maximize the metric.

Definition at line 99 of file silhouette_score.hpp.

The documentation for this class was generated from the following file:

src/mlpack/core/cv/metrics/silhouette_score.hpp

Static Public Member Functions

Static Public Attributes

Detailed Description

Member Function Documentation

◆ MeanDistanceFromCluster()

◆ Overall()

◆ SamplesScore() [1/2]

◆ SamplesScore() [2/2]

Member Data Documentation

◆ NeedsMinimization