als-statistics/docs/05-table/02-dbscan.md

A powerful and lightweight JavaScript library for descriptive statistics, regression, clustering, outlier detection, and noise analysis using a flexible table/column architecture.

## 📊 DBSCAN Clustering

`dbscan(eps, minPts)` identifies clusters of correlated numeric columns using DBSCAN (Density-Based Spatial Clustering of Applications with Noise).

### 🔹 Parameters
- `eps` (default: `0.4`) — maximum distance between columns to be considered neighbors.
- `minPts` (default: `3`) — minimum number of neighbors to form a dense region (cluster).

### 🔹 Usage

```js
const table = Statistics.newTable({
  A: [1, 2, 3],
  B: [10, 20, 30],
  C: [5, 10, 15]
});

const dbscan = table.dbscan(0.5, 2);
console.log(dbscan.labels);        // Example: [1, 1, -1]
console.log(dbscan.clusters);     // Array of clustered Table instances
```

### 🔹 Output
- `labels`: Array assigning a cluster ID or `-1` for noise to each column.
- `clusters`: Array of new `Table` instances, one per cluster.

### 🔹 How It Works
- Measures correlation between numeric columns.
- Builds pairwise distances.
- Expands clusters around dense areas.