als-statistics/lib/analyze/dbscan/dbscan.js

Modular JS statistics toolkit for Node.js and the browser: descriptive stats, correlations (Pearson/Spearman/Kendall), t-tests & ANOVA (Student/Welch), reliability (Cronbach’s alpha), regression (linear/logistic), clustering (DBSCAN/HDBSCAN), and table/co

import computeDistances from './compute-dist.js';
import { buildClusters } from './build-clusters.js'
import { TestBase } from '../test-base/index.js'
export default class Dbscan extends TestBase {
   constructor(samples, options = {}) {
      super(samples, 'Dbscan',[], { min: 2 })
      const { eps = 0.4, minPts = 3, metric = 'mad' } = options
      this.metric = metric;
      this.eps = eps;
      this.minPts = minPts;
      this.labels = new Array(this.k).fill(0); // 0: не обработан, -1: шум, 1+: кластер
      this.clusters = [];
      this.distances = computeDistances(this.samples,this.metric)
      this.run();
      buildClusters(this);
   }

   findNeighbors(pointIdx) {
      const neighbors = [];
      for (let i = 0; i < this.k; i++) {
         const d = this.distances[pointIdx][i];
         if (Number.isFinite(d) && d <= this.eps) neighbors.push(i);
      }
      return neighbors;
   }

   expandCluster(pointIdx, clusterId) {
      this.labels[pointIdx] = clusterId;
      const seeds = this.findNeighbors(pointIdx).filter(idx => idx !== pointIdx);
      while (seeds.length > 0) {
         const current = seeds.pop();
         if (this.labels[current] === -1) this.labels[current] = clusterId; // шум -> кластер
         if (this.labels[current] !== 0) continue; // уже помечен
         this.labels[current] = clusterId;
         const currentNeighbors = this.findNeighbors(current);
         if (currentNeighbors.length >= this.minPts) {
            for (const nb of currentNeighbors) {
               if (this.labels[nb] === 0) seeds.push(nb);
            }
         }
      }
   }

   run() { // Основной алгоритм DBSCAN
      let clusterId = 0;
      for (let i = 0; i < this.k; i++) {
         if (this.labels[i] !== 0) continue; // уже обработан

         const neighbors = this.findNeighbors(i);
         if (neighbors.length < this.minPts) {
            this.labels[i] = -1; // шум
            continue;
         }

         clusterId++;
         this.expandCluster(i, clusterId);
      }
   }
}