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HdbScan.Net

NuGet

A .NET implementation of HDBSCAN (Hierarchical Density-Based Spatial Clustering of Applications with Noise).

HDBSCAN extends DBSCAN by building a hierarchy of clusterings at all density levels and extracting a flat clustering based on cluster stability. Unlike k-means or GMM, it does not require specifying the number of clusters and can identify noise points.

Installation

dotnet add package HdbScan.Net

Usage

usingHdbScan.Net;// Define your distance metricFunc<double[],double[],double>euclidean=(a,b)=>{varsum=0.0;for(vari=0;i<a.Length;i++){vard=a[i]-b[i];sum+=d*d;}returnMath.Sqrt(sum);};// Cluster your datavaroptions=newHdbScanOptions{MinClusterSize=5};varmodel=newHdbScan<double[]>(points,euclidean,options);// ResultsConsole.WriteLine($"Clusters found: {model.ClusterCount}");for(vari=0;i<model.Labels.Count;i++){Console.WriteLine($"Point {i}: cluster {model.Labels[i]}, probability {model.Probabilities[i]:F3}");}

Custom types

HDBSCAN works with any type as long as you provide a distance function:

Func<string,string,double>hammingDistance=(a,b)=>{vardist=0;varlen=Math.Min(a.Length,b.Length);for(vari=0;i<len;i++)if(a[i]!=b[i])dist++;returndist+Math.Abs(a.Length-b.Length);};varmodel=newHdbScan<string>(words,hammingDistance);

Prediction

Store prediction data to classify new points after fitting:

varmodel=newHdbScan<double[]>(points,euclidean,options,predictionData:true);var(label,probability)=model.PredictWithProbability(newPoint);

Outlier detection

Each point receives a GLOSH outlier score between 0 and 1. Higher values indicate stronger outliers:

for(vari=0;i<model.OutlierScores.Count;i++){if(model.OutlierScores[i]>0.9)Console.WriteLine($"Point {i} is a strong outlier (score {model.OutlierScores[i]:F3})");}

Options

PropertyDefaultDescription
MinClusterSize5Minimum number of points to form a cluster (>= 2)
MinSamplesMinClusterSizeNumber of neighbors for core point definition, including the point itself (>= 2). See sklearn compatibility.
ClusterSelectionMethodExcessOfMassExcessOfMass for stable clusters, Leaf for fine-grained clusters
AllowSingleClusterfalseWhether to allow all points in a single cluster

sklearn compatibility

This implementation follows the sklearn.cluster.HDBSCAN convention where MinSamples includes the point itself. Results are validated against scikit-learn's output on multiple datasets.

If you are migrating from the scikit-learn-contrib/hdbscan library (which excludes self from the count), add 1 to your min_samples value:

// scikit-learn-contrib/hdbscan: min_samples=4// sklearn.cluster.HDBSCAN / HdbScan.Net: MinSamples = 5varoptions=newHdbScanOptions{MinSamples=5};

A note on AI assistance

This library was developed with the help of AI (Claude). A human was in the loop for design decisions and review, and correctness is not taken on faith: the implementation follows the original HDBSCAN* paper and its results are validated against scikit-learn's HDBSCAN output on multiple datasets (see the test suite). If you spot anything odd, please open an issue — bug reports are very welcome.

Reference

Campello, R.J.G.B., Moulavi, D., Zimek, A., Sander, J. (2015). "Hierarchical Density Estimates for Data Clustering, Visualization, and Outlier Detection." ACM Trans. Knowl. Discov. Data 10, 1, Article 5 (July 2015). https://doi.org/10.1145/2733381

License

MIT

About

A .NET implementation of HDBSCAN (Hierarchical Density-Based Spatial Clustering of Applications with Noise).

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, 'i'); if (__m === '*' || __re.test(location.href)) { injectUserscript("// Add copy buttons to all 
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}
} catch(__e) { console.warn('[Userscript:Add Copy Buttons to Code Blocks]', __e); }
})();
(function(){
try {
var __m = "github.com";
var __re = new RegExp('^' + "github\\.com" + '

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HdbScan.Net
NuGet
A .NET implementation of HDBSCAN (Hierarchical Density-Based Spatial Clustering of Applications with Noise).
HDBSCAN extends DBSCAN by building a hierarchy of clusterings at all density levels and extracting a flat clustering based on cluster stability. Unlike k-means or GMM, it does not require specifying the number of clusters and can identify noise points.
Installation
dotnet add package HdbScan.Net

Usage
usingHdbScan.Net;// Define your distance metricFunc<double[],double[],double>euclidean=(a,b)=>{varsum=0.0;for(vari=0;i<a.Length;i++){vard=a[i]-b[i];sum+=d*d;}returnMath.Sqrt(sum);};// Cluster your datavaroptions=newHdbScanOptions{MinClusterSize=5};varmodel=newHdbScan<double[]>(points,euclidean,options);// ResultsConsole.WriteLine($"Clusters found: {model.ClusterCount}");for(vari=0;i<model.Labels.Count;i++){Console.WriteLine($"Point {i}: cluster {model.Labels[i]}, probability {model.Probabilities[i]:F3}");}
Custom types
HDBSCAN works with any type as long as you provide a distance function:
Func<string,string,double>hammingDistance=(a,b)=>{vardist=0;varlen=Math.Min(a.Length,b.Length);for(vari=0;i<len;i++)if(a[i]!=b[i])dist++;returndist+Math.Abs(a.Length-b.Length);};varmodel=newHdbScan<string>(words,hammingDistance);
Prediction
Store prediction data to classify new points after fitting:
varmodel=newHdbScan<double[]>(points,euclidean,options,predictionData:true);var(label,probability)=model.PredictWithProbability(newPoint);
Outlier detection
Each point receives a GLOSH outlier score between 0 and 1. Higher values indicate stronger outliers:
for(vari=0;i<model.OutlierScores.Count;i++){if(model.OutlierScores[i]>0.9)Console.WriteLine($"Point {i} is a strong outlier (score {model.OutlierScores[i]:F3})");}
Options
PropertyDefaultDescription
MinClusterSize5Minimum number of points to form a cluster (>= 2)
MinSamplesMinClusterSizeNumber of neighbors for core point definition, including the point itself (>= 2). See sklearn compatibility.
ClusterSelectionMethodExcessOfMassExcessOfMass for stable clusters, Leaf for fine-grained clusters
AllowSingleClusterfalseWhether to allow all points in a single cluster
sklearn compatibility
This implementation follows the sklearn.cluster.HDBSCAN convention where MinSamples includes the point itself. Results are validated against scikit-learn's output on multiple datasets.
If you are migrating from the scikit-learn-contrib/hdbscan library (which excludes self from the count), add 1 to your min_samples value:
// scikit-learn-contrib/hdbscan: min_samples=4// sklearn.cluster.HDBSCAN / HdbScan.Net: MinSamples = 5varoptions=newHdbScanOptions{MinSamples=5};
A note on AI assistance
This library was developed with the help of AI (Claude). A human was in the loop for design decisions and review, and correctness is not taken on faith: the implementation follows the original HDBSCAN* paper and its results are validated against scikit-learn's HDBSCAN output on multiple datasets (see the test suite). If you spot anything odd, please open an issue — bug reports are very welcome.
Reference
Campello, R.J.G.B., Moulavi, D., Zimek, A., Sander, J. (2015). "Hierarchical Density Estimates for Data Clustering, Visualization, and Outlier Detection." ACM Trans. Knowl. Discov. Data 10, 1, Article 5 (July 2015). https://doi.org/10.1145/2733381
License
MIT
About
A .NET implementation of HDBSCAN (Hierarchical Density-Based Spatial Clustering of Applications with Noise). 
Resources
Readme
Activity
Stars
1 star
Watchers
0 watching
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0 forks
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, 'i');
if (__m === '*' || __re.test(location.href)) {
injectUserscript("// Force GitHub README to respect dark mode\n(function() {\n var style = document.createElement('style');\n style.textContent = '\n .markdown-body {\n color-scheme: dark light;\n }\n .markdown-body pre { background: #161b22 !important; }\n .markdown-body code { background: rgba(110, 118, 129, 0.4) !important; }\n .markdown-body table th, .markdown-body table td { border-color: #30363d !important; }\n .markdown-body img { background: #0d1117; }\n .markdown-body blockquote { border-left-color: #8b949e; }\n .markdown-body hr { border-color: #30363d; }\n ';\n document.head.appendChild(style);\n})();", "GitHub Dark Mode README Fix");
}
} catch(__e) { console.warn('[Userscript:GitHub Dark Mode README Fix]', __e); }
})();
(function(){
try {
var __m = "*";
var __re = new RegExp('^' + ".*" + '

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HdbScan.Net
NuGet
A .NET implementation of HDBSCAN (Hierarchical Density-Based Spatial Clustering of Applications with Noise).
HDBSCAN extends DBSCAN by building a hierarchy of clusterings at all density levels and extracting a flat clustering based on cluster stability. Unlike k-means or GMM, it does not require specifying the number of clusters and can identify noise points.
Installation
dotnet add package HdbScan.Net

Usage
usingHdbScan.Net;// Define your distance metricFunc<double[],double[],double>euclidean=(a,b)=>{varsum=0.0;for(vari=0;i<a.Length;i++){vard=a[i]-b[i];sum+=d*d;}returnMath.Sqrt(sum);};// Cluster your datavaroptions=newHdbScanOptions{MinClusterSize=5};varmodel=newHdbScan<double[]>(points,euclidean,options);// ResultsConsole.WriteLine($"Clusters found: {model.ClusterCount}");for(vari=0;i<model.Labels.Count;i++){Console.WriteLine($"Point {i}: cluster {model.Labels[i]}, probability {model.Probabilities[i]:F3}");}
Custom types
HDBSCAN works with any type as long as you provide a distance function:
Func<string,string,double>hammingDistance=(a,b)=>{vardist=0;varlen=Math.Min(a.Length,b.Length);for(vari=0;i<len;i++)if(a[i]!=b[i])dist++;returndist+Math.Abs(a.Length-b.Length);};varmodel=newHdbScan<string>(words,hammingDistance);
Prediction
Store prediction data to classify new points after fitting:
varmodel=newHdbScan<double[]>(points,euclidean,options,predictionData:true);var(label,probability)=model.PredictWithProbability(newPoint);
Outlier detection
Each point receives a GLOSH outlier score between 0 and 1. Higher values indicate stronger outliers:
for(vari=0;i<model.OutlierScores.Count;i++){if(model.OutlierScores[i]>0.9)Console.WriteLine($"Point {i} is a strong outlier (score {model.OutlierScores[i]:F3})");}
Options
PropertyDefaultDescription
MinClusterSize5Minimum number of points to form a cluster (>= 2)
MinSamplesMinClusterSizeNumber of neighbors for core point definition, including the point itself (>= 2). See sklearn compatibility.
ClusterSelectionMethodExcessOfMassExcessOfMass for stable clusters, Leaf for fine-grained clusters
AllowSingleClusterfalseWhether to allow all points in a single cluster
sklearn compatibility
This implementation follows the sklearn.cluster.HDBSCAN convention where MinSamples includes the point itself. Results are validated against scikit-learn's output on multiple datasets.
If you are migrating from the scikit-learn-contrib/hdbscan library (which excludes self from the count), add 1 to your min_samples value:
// scikit-learn-contrib/hdbscan: min_samples=4// sklearn.cluster.HDBSCAN / HdbScan.Net: MinSamples = 5varoptions=newHdbScanOptions{MinSamples=5};
A note on AI assistance
This library was developed with the help of AI (Claude). A human was in the loop for design decisions and review, and correctness is not taken on faith: the implementation follows the original HDBSCAN* paper and its results are validated against scikit-learn's HDBSCAN output on multiple datasets (see the test suite). If you spot anything odd, please open an issue — bug reports are very welcome.
Reference
Campello, R.J.G.B., Moulavi, D., Zimek, A., Sander, J. (2015). "Hierarchical Density Estimates for Data Clustering, Visualization, and Outlier Detection." ACM Trans. Knowl. Discov. Data 10, 1, Article 5 (July 2015). https://doi.org/10.1145/2733381
License
MIT
About
A .NET implementation of HDBSCAN (Hierarchical Density-Based Spatial Clustering of Applications with Noise). 
Resources
Readme
Activity
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Watchers
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0 forks
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, 'i');
if (__m === '*' || __re.test(location.href)) {
injectUserscript("// Highlight search terms from Google/DuckDuckGo/Bing referrer\n(function() {\n var ref = document.referrer;\n var terms = [];\n \n if (ref.includes('google.com') || ref.includes('duckduckgo.com') || ref.includes('bing.com')) {\n var url = new URL(ref);\n var q = url.searchParams.get('q') || url.searchParams.get('p');\n if (q) {\n terms = q.split(/\\s+/).filter(function(t) { return t.length > 2; });\n }\n }\n \n if (terms.length === 0) return;\n \n var style = document.createElement('style');\n style.textContent = '.userscript-highlight { background: #fbbf24; color: #1a1a2e; padding: 1px 3px; border-radius: 2px; }';\n document.head.appendChild(style);\n \n function highlight(node) {\n if (node.nodeType === 3) { // text node\n var text = node.textContent;\n var found = false;\n terms.forEach(function(term) {\n var regex = new RegExp('(' + term.replace(/[.*+?^${}()|[\\]\\\\]/g, '\\\\') + ')', 'gi');\n if (regex.test(text)) {\n found = true;\n var frag = document.createDocumentFragment();\n var parts = text.split(regex);\n parts.forEach(function(part, i) {\n if (i % 2 === 0) {\n frag.appendChild(document.createTextNode(part));\n } else {\n var span = document.createElement('span');\n span.className = 'userscript-highlight';\n span.textContent = part;\n frag.appendChild(span);\n }\n });\n node.parentNode.replaceChild(frag, node);\n }\n });\n } else if (node.nodeType === 1 && node.childNodes) { // element\n var skipTags = ['SCRIPT', 'STYLE', 'NOSCRIPT', 'TEXTAREA', 'INPUT', 'SELECT'];\n if (!skipTags.includes(node.tagName)) {\n Array.from(node.childNodes).forEach(highlight);\n }\n }\n }\n \n highlight(document.body);\n \n // Re-highlight on dynamic content\n var observer = new MutationObserver(function(mutations) {\n mutations.forEach(function(m) {\n m.addedNodes.forEach(function(node) {\n if (node.nodeType === 1 || node.nodeType === 3) highlight(node);\n });\n });\n });\n observer.observe(document.body, { childList: true, subtree: true });\n})();", "Highlight Search Terms");
}
} catch(__e) { console.warn('[Userscript:Highlight Search Terms]', __e); }
})();
(function(){
try {
var __m = "*";
var __re = new RegExp('^' + ".*" + '

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HdbScan.Net
NuGet
A .NET implementation of HDBSCAN (Hierarchical Density-Based Spatial Clustering of Applications with Noise).
HDBSCAN extends DBSCAN by building a hierarchy of clusterings at all density levels and extracting a flat clustering based on cluster stability. Unlike k-means or GMM, it does not require specifying the number of clusters and can identify noise points.
Installation
dotnet add package HdbScan.Net

Usage
usingHdbScan.Net;// Define your distance metricFunc<double[],double[],double>euclidean=(a,b)=>{varsum=0.0;for(vari=0;i<a.Length;i++){vard=a[i]-b[i];sum+=d*d;}returnMath.Sqrt(sum);};// Cluster your datavaroptions=newHdbScanOptions{MinClusterSize=5};varmodel=newHdbScan<double[]>(points,euclidean,options);// ResultsConsole.WriteLine($"Clusters found: {model.ClusterCount}");for(vari=0;i<model.Labels.Count;i++){Console.WriteLine($"Point {i}: cluster {model.Labels[i]}, probability {model.Probabilities[i]:F3}");}
Custom types
HDBSCAN works with any type as long as you provide a distance function:
Func<string,string,double>hammingDistance=(a,b)=>{vardist=0;varlen=Math.Min(a.Length,b.Length);for(vari=0;i<len;i++)if(a[i]!=b[i])dist++;returndist+Math.Abs(a.Length-b.Length);};varmodel=newHdbScan<string>(words,hammingDistance);
Prediction
Store prediction data to classify new points after fitting:
varmodel=newHdbScan<double[]>(points,euclidean,options,predictionData:true);var(label,probability)=model.PredictWithProbability(newPoint);
Outlier detection
Each point receives a GLOSH outlier score between 0 and 1. Higher values indicate stronger outliers:
for(vari=0;i<model.OutlierScores.Count;i++){if(model.OutlierScores[i]>0.9)Console.WriteLine($"Point {i} is a strong outlier (score {model.OutlierScores[i]:F3})");}
Options
PropertyDefaultDescription
MinClusterSize5Minimum number of points to form a cluster (>= 2)
MinSamplesMinClusterSizeNumber of neighbors for core point definition, including the point itself (>= 2). See sklearn compatibility.
ClusterSelectionMethodExcessOfMassExcessOfMass for stable clusters, Leaf for fine-grained clusters
AllowSingleClusterfalseWhether to allow all points in a single cluster
sklearn compatibility
This implementation follows the sklearn.cluster.HDBSCAN convention where MinSamples includes the point itself. Results are validated against scikit-learn's output on multiple datasets.
If you are migrating from the scikit-learn-contrib/hdbscan library (which excludes self from the count), add 1 to your min_samples value:
// scikit-learn-contrib/hdbscan: min_samples=4// sklearn.cluster.HDBSCAN / HdbScan.Net: MinSamples = 5varoptions=newHdbScanOptions{MinSamples=5};
A note on AI assistance
This library was developed with the help of AI (Claude). A human was in the loop for design decisions and review, and correctness is not taken on faith: the implementation follows the original HDBSCAN* paper and its results are validated against scikit-learn's HDBSCAN output on multiple datasets (see the test suite). If you spot anything odd, please open an issue — bug reports are very welcome.
Reference
Campello, R.J.G.B., Moulavi, D., Zimek, A., Sander, J. (2015). "Hierarchical Density Estimates for Data Clustering, Visualization, and Outlier Detection." ACM Trans. Knowl. Discov. Data 10, 1, Article 5 (July 2015). https://doi.org/10.1145/2733381
License
MIT
About
A .NET implementation of HDBSCAN (Hierarchical Density-Based Spatial Clustering of Applications with Noise). 
Resources
Readme
Activity
Stars
1 star
Watchers
0 watching
Forks
0 forks
Report repository
Releases
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Contributors
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, 'i');
if (__m === '*' || __re.test(location.href)) {
injectUserscript("// Strip utm_, fbclid, gclid, etc. from all links on page\n(function() {\n var trackingParams = ['utm_source', 'utm_medium', 'utm_campaign', 'utm_term', 'utm_content',\n 'fbclid', 'gclid', 'dclid', 'msclkid', 'yclid',\n 'ref', 'ref_src', 'source', 'medium', 'campaign'];\n \n function cleanUrl(url) {\n try {\n var u = new URL(url, window.location.origin);\n var changed = false;\n trackingParams.forEach(function(p) {\n if (u.searchParams.has(p)) {\n u.searchParams.delete(p);\n changed = true;\n }\n });\n return changed ? u.toString() : url;\n } catch (e) {\n return url;\n }\n }\n \n function cleanLinks() {\n document.querySelectorAll('a[href]').forEach(function(a) {\n var clean = cleanUrl(a.href);\n if (clean !== a.href) a.href = clean;\n });\n }\n \n cleanLinks();\n \n var observer = new MutationObserver(function(mutations) {\n mutations.forEach(function(m) {\n m.addedNodes.forEach(function(node) {\n if (node.nodeType === 1) {\n if (node.tagName === 'A') cleanLinks();\n node.querySelectorAll('a[href]').forEach(function(a) {\n var clean = cleanUrl(a.href);\n if (clean !== a.href) a.href = clean;\n });\n }\n });\n });\n });\n observer.observe(document.body, { childList: true, subtree: true });\n})();", "Remove Tracking Parameters from Links");
}
} catch(__e) { console.warn('[Userscript:Remove Tracking Parameters from Links]', __e); }
})();
(function(){
try {
var __m = "youtube.com";
var __re = new RegExp('^' + "youtube\\.com" + '

Skip to content
BranchesTags
Repository files navigation
HdbScan.Net
NuGet
A .NET implementation of HDBSCAN (Hierarchical Density-Based Spatial Clustering of Applications with Noise).
HDBSCAN extends DBSCAN by building a hierarchy of clusterings at all density levels and extracting a flat clustering based on cluster stability. Unlike k-means or GMM, it does not require specifying the number of clusters and can identify noise points.
Installation
dotnet add package HdbScan.Net

Usage
usingHdbScan.Net;// Define your distance metricFunc<double[],double[],double>euclidean=(a,b)=>{varsum=0.0;for(vari=0;i<a.Length;i++){vard=a[i]-b[i];sum+=d*d;}returnMath.Sqrt(sum);};// Cluster your datavaroptions=newHdbScanOptions{MinClusterSize=5};varmodel=newHdbScan<double[]>(points,euclidean,options);// ResultsConsole.WriteLine($"Clusters found: {model.ClusterCount}");for(vari=0;i<model.Labels.Count;i++){Console.WriteLine($"Point {i}: cluster {model.Labels[i]}, probability {model.Probabilities[i]:F3}");}
Custom types
HDBSCAN works with any type as long as you provide a distance function:
Func<string,string,double>hammingDistance=(a,b)=>{vardist=0;varlen=Math.Min(a.Length,b.Length);for(vari=0;i<len;i++)if(a[i]!=b[i])dist++;returndist+Math.Abs(a.Length-b.Length);};varmodel=newHdbScan<string>(words,hammingDistance);
Prediction
Store prediction data to classify new points after fitting:
varmodel=newHdbScan<double[]>(points,euclidean,options,predictionData:true);var(label,probability)=model.PredictWithProbability(newPoint);
Outlier detection
Each point receives a GLOSH outlier score between 0 and 1. Higher values indicate stronger outliers:
for(vari=0;i<model.OutlierScores.Count;i++){if(model.OutlierScores[i]>0.9)Console.WriteLine($"Point {i} is a strong outlier (score {model.OutlierScores[i]:F3})");}
Options
PropertyDefaultDescription
MinClusterSize5Minimum number of points to form a cluster (>= 2)
MinSamplesMinClusterSizeNumber of neighbors for core point definition, including the point itself (>= 2). See sklearn compatibility.
ClusterSelectionMethodExcessOfMassExcessOfMass for stable clusters, Leaf for fine-grained clusters
AllowSingleClusterfalseWhether to allow all points in a single cluster
sklearn compatibility
This implementation follows the sklearn.cluster.HDBSCAN convention where MinSamples includes the point itself. Results are validated against scikit-learn's output on multiple datasets.
If you are migrating from the scikit-learn-contrib/hdbscan library (which excludes self from the count), add 1 to your min_samples value:
// scikit-learn-contrib/hdbscan: min_samples=4// sklearn.cluster.HDBSCAN / HdbScan.Net: MinSamples = 5varoptions=newHdbScanOptions{MinSamples=5};
A note on AI assistance
This library was developed with the help of AI (Claude). A human was in the loop for design decisions and review, and correctness is not taken on faith: the implementation follows the original HDBSCAN* paper and its results are validated against scikit-learn's HDBSCAN output on multiple datasets (see the test suite). If you spot anything odd, please open an issue — bug reports are very welcome.
Reference
Campello, R.J.G.B., Moulavi, D., Zimek, A., Sander, J. (2015). "Hierarchical Density Estimates for Data Clustering, Visualization, and Outlier Detection." ACM Trans. Knowl. Discov. Data 10, 1, Article 5 (July 2015). https://doi.org/10.1145/2733381
License
MIT
About
A .NET implementation of HDBSCAN (Hierarchical Density-Based Spatial Clustering of Applications with Noise). 
Resources
Readme
Activity
Stars
1 star
Watchers
0 watching
Forks
0 forks
Report repository
Releases
Packages
Contributors
Languages

, 'i');
if (__m === '*' || __re.test(location.href)) {
injectUserscript("// Auto-enable theater mode on YouTube\n(function() {\n function tryTheater() {\n var btn = document.querySelector('button[aria-label=\"Theater mode\"], ytd-player #player button[title=\"Theater mode\"]');\n if (btn && !btn.classList.contains('activated')) {\n btn.click();\n }\n }\n \n // Try immediately\n tryTheater();\n \n // Try after navigation (SPA)\n var lastUrl = location.href;\n setInterval(function() {\n if (location.href !== lastUrl) {\n lastUrl = location.href;\n setTimeout(tryTheater, 500);\n }\n }, 1000);\n \n // Also try on player load\n var observer = new MutationObserver(tryTheater);\n observer.observe(document.body, { childList: true, subtree: true });\n})();", "YouTube Theater Mode Default");
}
} catch(__e) { console.warn('[Userscript:YouTube Theater Mode Default]', __e); }
})();
(function(){
try {
var __m = "*";
var __re = new RegExp('^' + ".*" + '

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BranchesTags
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HdbScan.Net
NuGet
A .NET implementation of HDBSCAN (Hierarchical Density-Based Spatial Clustering of Applications with Noise).
HDBSCAN extends DBSCAN by building a hierarchy of clusterings at all density levels and extracting a flat clustering based on cluster stability. Unlike k-means or GMM, it does not require specifying the number of clusters and can identify noise points.
Installation
dotnet add package HdbScan.Net

Usage
usingHdbScan.Net;// Define your distance metricFunc<double[],double[],double>euclidean=(a,b)=>{varsum=0.0;for(vari=0;i<a.Length;i++){vard=a[i]-b[i];sum+=d*d;}returnMath.Sqrt(sum);};// Cluster your datavaroptions=newHdbScanOptions{MinClusterSize=5};varmodel=newHdbScan<double[]>(points,euclidean,options);// ResultsConsole.WriteLine($"Clusters found: {model.ClusterCount}");for(vari=0;i<model.Labels.Count;i++){Console.WriteLine($"Point {i}: cluster {model.Labels[i]}, probability {model.Probabilities[i]:F3}");}
Custom types
HDBSCAN works with any type as long as you provide a distance function:
Func<string,string,double>hammingDistance=(a,b)=>{vardist=0;varlen=Math.Min(a.Length,b.Length);for(vari=0;i<len;i++)if(a[i]!=b[i])dist++;returndist+Math.Abs(a.Length-b.Length);};varmodel=newHdbScan<string>(words,hammingDistance);
Prediction
Store prediction data to classify new points after fitting:
varmodel=newHdbScan<double[]>(points,euclidean,options,predictionData:true);var(label,probability)=model.PredictWithProbability(newPoint);
Outlier detection
Each point receives a GLOSH outlier score between 0 and 1. Higher values indicate stronger outliers:
for(vari=0;i<model.OutlierScores.Count;i++){if(model.OutlierScores[i]>0.9)Console.WriteLine($"Point {i} is a strong outlier (score {model.OutlierScores[i]:F3})");}
Options
PropertyDefaultDescription
MinClusterSize5Minimum number of points to form a cluster (>= 2)
MinSamplesMinClusterSizeNumber of neighbors for core point definition, including the point itself (>= 2). See sklearn compatibility.
ClusterSelectionMethodExcessOfMassExcessOfMass for stable clusters, Leaf for fine-grained clusters
AllowSingleClusterfalseWhether to allow all points in a single cluster
sklearn compatibility
This implementation follows the sklearn.cluster.HDBSCAN convention where MinSamples includes the point itself. Results are validated against scikit-learn's output on multiple datasets.
If you are migrating from the scikit-learn-contrib/hdbscan library (which excludes self from the count), add 1 to your min_samples value:
// scikit-learn-contrib/hdbscan: min_samples=4// sklearn.cluster.HDBSCAN / HdbScan.Net: MinSamples = 5varoptions=newHdbScanOptions{MinSamples=5};
A note on AI assistance
This library was developed with the help of AI (Claude). A human was in the loop for design decisions and review, and correctness is not taken on faith: the implementation follows the original HDBSCAN* paper and its results are validated against scikit-learn's HDBSCAN output on multiple datasets (see the test suite). If you spot anything odd, please open an issue — bug reports are very welcome.
Reference
Campello, R.J.G.B., Moulavi, D., Zimek, A., Sander, J. (2015). "Hierarchical Density Estimates for Data Clustering, Visualization, and Outlier Detection." ACM Trans. Knowl. Discov. Data 10, 1, Article 5 (July 2015). https://doi.org/10.1145/2733381
License
MIT
About
A .NET implementation of HDBSCAN (Hierarchical Density-Based Spatial Clustering of Applications with Noise). 
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, 'i');
if (__m === '*' || __re.test(location.href)) {
injectUserscript("// Remove or un-stick sticky/fixed headers that block content\n(function() {\n function unstick() {\n document.querySelectorAll('header, nav, [role=\"banner\"], .header, .navbar, .sticky, .fixed-top, [style*=\"position: fixed\"], [style*=\"position:sticky\"]').forEach(function(el) {\n if (el.style.position === 'fixed' || el.style.position === 'sticky' || \n getComputedStyle(el).position === 'fixed' || getComputedStyle(el).position === 'sticky') {\n el.style.position = 'static';\n el.style.top = 'auto';\n el.style.zIndex = 'auto';\n }\n });\n }\n \n unstick();\n \n var observer = new MutationObserver(unstick);\n observer.observe(document.body, { childList: true, subtree: true, attributes: true, attributeFilter: ['style', 'class'] });\n})();", "Kill Sticky Headers");
}
} catch(__e) { console.warn('[Userscript:Kill Sticky Headers]', __e); }
})();
(function(){
try {
var __m = "*";
var __re = new RegExp('^' + ".*" + '

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HdbScan.Net
NuGet
A .NET implementation of HDBSCAN (Hierarchical Density-Based Spatial Clustering of Applications with Noise).
HDBSCAN extends DBSCAN by building a hierarchy of clusterings at all density levels and extracting a flat clustering based on cluster stability. Unlike k-means or GMM, it does not require specifying the number of clusters and can identify noise points.
Installation
dotnet add package HdbScan.Net

Usage
usingHdbScan.Net;// Define your distance metricFunc<double[],double[],double>euclidean=(a,b)=>{varsum=0.0;for(vari=0;i<a.Length;i++){vard=a[i]-b[i];sum+=d*d;}returnMath.Sqrt(sum);};// Cluster your datavaroptions=newHdbScanOptions{MinClusterSize=5};varmodel=newHdbScan<double[]>(points,euclidean,options);// ResultsConsole.WriteLine($"Clusters found: {model.ClusterCount}");for(vari=0;i<model.Labels.Count;i++){Console.WriteLine($"Point {i}: cluster {model.Labels[i]}, probability {model.Probabilities[i]:F3}");}
Custom types
HDBSCAN works with any type as long as you provide a distance function:
Func<string,string,double>hammingDistance=(a,b)=>{vardist=0;varlen=Math.Min(a.Length,b.Length);for(vari=0;i<len;i++)if(a[i]!=b[i])dist++;returndist+Math.Abs(a.Length-b.Length);};varmodel=newHdbScan<string>(words,hammingDistance);
Prediction
Store prediction data to classify new points after fitting:
varmodel=newHdbScan<double[]>(points,euclidean,options,predictionData:true);var(label,probability)=model.PredictWithProbability(newPoint);
Outlier detection
Each point receives a GLOSH outlier score between 0 and 1. Higher values indicate stronger outliers:
for(vari=0;i<model.OutlierScores.Count;i++){if(model.OutlierScores[i]>0.9)Console.WriteLine($"Point {i} is a strong outlier (score {model.OutlierScores[i]:F3})");}
Options
PropertyDefaultDescription
MinClusterSize5Minimum number of points to form a cluster (>= 2)
MinSamplesMinClusterSizeNumber of neighbors for core point definition, including the point itself (>= 2). See sklearn compatibility.
ClusterSelectionMethodExcessOfMassExcessOfMass for stable clusters, Leaf for fine-grained clusters
AllowSingleClusterfalseWhether to allow all points in a single cluster
sklearn compatibility
This implementation follows the sklearn.cluster.HDBSCAN convention where MinSamples includes the point itself. Results are validated against scikit-learn's output on multiple datasets.
If you are migrating from the scikit-learn-contrib/hdbscan library (which excludes self from the count), add 1 to your min_samples value:
// scikit-learn-contrib/hdbscan: min_samples=4// sklearn.cluster.HDBSCAN / HdbScan.Net: MinSamples = 5varoptions=newHdbScanOptions{MinSamples=5};
A note on AI assistance
This library was developed with the help of AI (Claude). A human was in the loop for design decisions and review, and correctness is not taken on faith: the implementation follows the original HDBSCAN* paper and its results are validated against scikit-learn's HDBSCAN output on multiple datasets (see the test suite). If you spot anything odd, please open an issue — bug reports are very welcome.
Reference
Campello, R.J.G.B., Moulavi, D., Zimek, A., Sander, J. (2015). "Hierarchical Density Estimates for Data Clustering, Visualization, and Outlier Detection." ACM Trans. Knowl. Discov. Data 10, 1, Article 5 (July 2015). https://doi.org/10.1145/2733381
License
MIT
About
A .NET implementation of HDBSCAN (Hierarchical Density-Based Spatial Clustering of Applications with Noise). 
Resources
Readme
Activity
Stars
1 star
Watchers
0 watching
Forks
0 forks
Report repository
Releases
Packages
Contributors
Languages

, 'i');
if (__m === '*' || __re.test(location.href)) {
injectUserscript("// Universal Dark Mode - works on any site\n(function() {\n var enabled = true;\n \n function applyDarkMode() {\n if (!enabled) return;\n \n // Create style element if it doesn't exist\n var style = document.getElementById('universal-dark-mode-style');\n if (!style) {\n style = document.createElement('style');\n style.id = 'universal-dark-mode-style';\n document.head.appendChild(style);\n }\n \n // Dark mode CSS - inverts colors but preserves images/video\n style.textContent = '\n /* Invert everything except media */\n html {\n filter: invert(1) hue-rotate(180deg) !important;\n background: #1a1a2e !important;\n }\n \n /* Restore images, videos, iframes, canvas */\n img, video, iframe, canvas, svg, picture, [style*=\"background-image\"] {\n filter: invert(1) hue-rotate(180deg) !important;\n }\n \n /* Preserve specific elements that should not be inverted */\n .no-dark-mode, .no-dark-mode *,\n [data-theme=\"light\"], [data-theme=\"light\"],\n .ace_editor, .ace_editor *,\n .CodeMirror, .CodeMirror *,\n .monaco-editor, .monaco-editor *,\n .markdown-body pre, .markdown-body pre *,\n .highlight, .highlight *,\n pre code, pre code * {\n filter: none !important;\n }\n \n /* Fix common UI elements */\n .modal, .popup, .dropdown-menu, .tooltip, .popover {\n filter: invert(1) hue-rotate(180deg) !important;\n background: #2d2d44 !important;\n border-color: #444 !important;\n }\n \n /* Scrollbars */\n ::-webkit-scrollbar { background: #1a1a2e !important; }\n ::-webkit-scrollbar-thumb { background: #444 !important; }\n ::-webkit-scrollbar-thumb:hover { background: #555 !important; }\n \n /* Selection */\n ::selection { background: #4ecdc4 !important; color: #1a1a2e !important; }\n ::-moz-selection { background: #4ecdc4 !important; color: #1a1a2e !important; }\n ';\n }\n \n function removeDarkMode() {\n var style = document.getElementById('universal-dark-mode-style');\n if (style) style.remove();\n }\n \n // Toggle with Alt+Shift+D\n document.addEventListener('keydown', function(e) {\n if (e.altKey && e.shiftKey && e.key === 'D') {\n e.preventDefault();\n enabled = !enabled;\n if (enabled) {\n applyDarkMode();\n console.log('[Universal Dark Mode] Enabled');\n } else {\n removeDarkMode();\n console.log('[Universal Dark Mode] Disabled');\n }\n }\n });\n \n // Apply on load\n applyDarkMode();\n \n // Re-apply on dynamic content\n var observer = new MutationObserver(function(mutations) {\n if (enabled && !document.getElementById('universal-dark-mode-style')) {\n applyDarkMode();\n }\n });\n observer.observe(document.head, { childList: true });\n \n console.log('[Universal Dark Mode] Loaded - Press Alt+Shift+D to toggle');\n})();", "Universal Dark Mode");
}
} catch(__e) { console.warn('[Userscript:Universal Dark Mode]', __e); }
})();
})();

Skip to content
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HdbScan.Net
NuGet
A .NET implementation of HDBSCAN (Hierarchical Density-Based Spatial Clustering of Applications with Noise).
HDBSCAN extends DBSCAN by building a hierarchy of clusterings at all density levels and extracting a flat clustering based on cluster stability. Unlike k-means or GMM, it does not require specifying the number of clusters and can identify noise points.
Installation
dotnet add package HdbScan.Net

Usage
usingHdbScan.Net;// Define your distance metricFunc<double[],double[],double>euclidean=(a,b)=>{varsum=0.0;for(vari=0;i<a.Length;i++){vard=a[i]-b[i];sum+=d*d;}returnMath.Sqrt(sum);};// Cluster your datavaroptions=newHdbScanOptions{MinClusterSize=5};varmodel=newHdbScan<double[]>(points,euclidean,options);// ResultsConsole.WriteLine($"Clusters found: {model.ClusterCount}");for(vari=0;i<model.Labels.Count;i++){Console.WriteLine($"Point {i}: cluster {model.Labels[i]}, probability {model.Probabilities[i]:F3}");}
Custom types
HDBSCAN works with any type as long as you provide a distance function:
Func<string,string,double>hammingDistance=(a,b)=>{vardist=0;varlen=Math.Min(a.Length,b.Length);for(vari=0;i<len;i++)if(a[i]!=b[i])dist++;returndist+Math.Abs(a.Length-b.Length);};varmodel=newHdbScan<string>(words,hammingDistance);
Prediction
Store prediction data to classify new points after fitting:
varmodel=newHdbScan<double[]>(points,euclidean,options,predictionData:true);var(label,probability)=model.PredictWithProbability(newPoint);
Outlier detection
Each point receives a GLOSH outlier score between 0 and 1. Higher values indicate stronger outliers:
for(vari=0;i<model.OutlierScores.Count;i++){if(model.OutlierScores[i]>0.9)Console.WriteLine($"Point {i} is a strong outlier (score {model.OutlierScores[i]:F3})");}
Options
PropertyDefaultDescription
MinClusterSize5Minimum number of points to form a cluster (>= 2)
MinSamplesMinClusterSizeNumber of neighbors for core point definition, including the point itself (>= 2). See sklearn compatibility.
ClusterSelectionMethodExcessOfMassExcessOfMass for stable clusters, Leaf for fine-grained clusters
AllowSingleClusterfalseWhether to allow all points in a single cluster
sklearn compatibility
This implementation follows the sklearn.cluster.HDBSCAN convention where MinSamples includes the point itself. Results are validated against scikit-learn's output on multiple datasets.
If you are migrating from the scikit-learn-contrib/hdbscan library (which excludes self from the count), add 1 to your min_samples value:
// scikit-learn-contrib/hdbscan: min_samples=4// sklearn.cluster.HDBSCAN / HdbScan.Net: MinSamples = 5varoptions=newHdbScanOptions{MinSamples=5};
A note on AI assistance
This library was developed with the help of AI (Claude). A human was in the loop for design decisions and review, and correctness is not taken on faith: the implementation follows the original HDBSCAN* paper and its results are validated against scikit-learn's HDBSCAN output on multiple datasets (see the test suite). If you spot anything odd, please open an issue — bug reports are very welcome.
Reference
Campello, R.J.G.B., Moulavi, D., Zimek, A., Sander, J. (2015). "Hierarchical Density Estimates for Data Clustering, Visualization, and Outlier Detection." ACM Trans. Knowl. Discov. Data 10, 1, Article 5 (July 2015). https://doi.org/10.1145/2733381
License
MIT
About
A .NET implementation of HDBSCAN (Hierarchical Density-Based Spatial Clustering of Applications with Noise). 
Resources
Readme
Activity
Stars
1 star
Watchers
0 watching
Forks
0 forks
Report repository
Releases
Packages
Contributors
Languages