# silhouette

package module
Version: v0.0.0-...-9bb9963 Latest Latest Go to latest
Published: Sep 18, 2019 License: MIT

## 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"
)

// 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
...
}
``````

## Documentation ¶

### Overview ¶

Package silhouette implements the silhouette cluster analysis algorithm See: https://en.wikipedia.org/wiki/Silhouette_(clustering)

### Constants ¶

This section is empty.

### Variables ¶

This section is empty.

### Functions ¶

#### func EstimateK ¶

`func EstimateK(data clusters.Observations, kmax int, m Partitioner) (int, float64, error)`

EstimateK estimates the amount of clusters (k) along with the silhouette score for that value, using the given partitioning algorithm

#### func Plot ¶

`func Plot(filename string, scores []KScore) error`

Plot creates a graph of the silhouette scores

#### func Score ¶

`func Score(data clusters.Observations, k int, m Partitioner) (float64, error)`

Score calculates the silhouette score for a given value of k, using the given partitioning algorithm

### Types ¶

#### type KScore ¶

```type KScore struct {
K     int
Score float64
}```

KScore holds the score for a value of K

#### func Scores ¶

`func Scores(data clusters.Observations, kmax int, m Partitioner) ([]KScore, error)`

Scores calculates the silhouette scores for all values of k between 2 and kmax, using the given partitioning algorithm

#### type Partitioner ¶

```type Partitioner interface {
Partition(data clusters.Observations, k int) (clusters.Clusters, error)
}```

Partitioner interface which suitable clustering algorithms should implement