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https://github.com/muesli/silhouette
Silhouette cluster analysis implementation in Go
https://github.com/muesli/silhouette
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Silhouette cluster analysis implementation in Go
- Host: GitHub
- URL: https://github.com/muesli/silhouette
- Owner: muesli
- License: mit
- Created: 2018-06-05T18:35:56.000Z (over 6 years ago)
- Default Branch: master
- Last Pushed: 2019-09-18T00:29:19.000Z (about 5 years ago)
- Last Synced: 2024-10-04T16:45:38.065Z (about 1 month ago)
- Topics: hacktoberfest
- Language: Go
- Size: 17.6 KB
- Stars: 11
- Watchers: 3
- Forks: 0
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
- License: LICENSE
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README
# silhouette
Silhouette cluster analysis implementation in Go
## What It Does
Silhouette refers to an [algorithm](https://en.wikipedia.org/wiki/Silhouette_(clustering))
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
```go
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
...
}
```
## Development
[](https://godoc.org/github.com/muesli/silhouette)
[](https://travis-ci.org/muesli/silhouette)
[](https://coveralls.io/github/muesli/silhouette?branch=master)
[](http://goreportcard.com/report/muesli/silhouette)