sil
github.com/muesli/silhouette
go get github.com/muesli/silhouette
sil

github.com/muesli/silhouette

Silhouette cluster analysis implementation in Go

by Christian Muehlhaeuser

v0.0.0-20190918002912-9bb99635ab9c (see all)License:MIT
go get github.com/muesli/silhouette
Readme

silhouette

Silhouette cluster analysis implementation in Go

What It Does

Silhouette refers to an algorithm used to interpret and validate the consistency within clusters of data.

The silhouette value is a measure of how similar an object is to its own cluster compared to other clusters. The silhouette ranges from −1 to +1, where a high value indicates that the object is well matched to its own cluster and poorly matched to neighboring clusters.

If most objects have a high value, then the clustering configuration is appropriate. If many points have a low or negative value, then the clustering configuration may have too many or too few clusters.

When You Should Use It

  • When you have numeric, multi-dimensional data sets
  • If you want to check whether your data set is clustered
  • When you have a vague idea of the clustering in your data set
  • You want to figure out the optimal clustering configuration

Example

import (
    "github.com/muesli/silhouette"
    "github.com/muesli/clusters"
    "github.com/muesli/kmeans"
)

// initialize your data set
// for the example we'll use three distinct clusters of data points
var d clusters.Observations
for x := 0; x < 64; x++ {
    d = append(d, clusters.Coordinates{
        rand.Float64() * 0.1,
        rand.Float64() * 0.1,
    })
}
for x := 0; x < 64; x++ {
    d = append(d, clusters.Coordinates{
        0.5 + rand.Float64()*0.1,
        0.5 + rand.Float64()*0.1,
    })
}
for x := 0; x < 64; x++ {
    d = append(d, clusters.Coordinates{
        0.9 + rand.Float64()*0.1,
        0.9 + rand.Float64()*0.1,
    })
}

// silhouette will theoretically work with multiple clustering algorithms
// it's commonly used with k-means
km := kmeans.New()

// compute the average silhouette score (coefficient) for 2 to 8 clusters, using
// the k-means clustering algorithm
scores, err := silhouette.Scores(d, 8, km)
for _, s := range scores {
    fmt.Printf("k: %d (score: %.2f)\n", s.K, s.Score)
}

// estimate the amount of clusters in our data set
// this returns the k with the highest score (where 2 <= k <= 8)
k, score, err := silhouette.EstimateK(d, 8, km)

// k is usually 3 for this example, with a score close to 1.0
// note that k-means doesn't always converge optimally
...
}

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3yrs ago

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VersionTagPublished
v0.0.0-20190918002912-9bb99635ab9c
2yrs ago
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