entropyx/dist/clustering/dbscan.js

A simple data mining library, written in TypeScript

"use strict";
Object.defineProperty(exports, "__esModule", { value: true });
exports.DBSCAN = void 0;
const distance_1 = require("@/base/distance");
const lodash_1 = require("lodash");
class DBSCAN {
    constructor(options = {}) {
        this.options = options;
        this.points = [];
        this.visited = [];
        this.assigned = [];
        this.clusters = [];
        if (!this.options.epsilon)
            this.options.epsilon = 0.2;
        if (!this.options.minPoints)
            this.options.minPoints = 20;
        if (!this.options.distance)
            this.options.distance = distance_1.Distance.euclidean;
    }
    fit(data) {
        this.points = data;
        this.visited = Array(data.length).fill(false);
        this.assigned = Array(data.length).fill(false);
        this.clusters = [];
        const { epsilon, minPoints, distance } = this.options;
        for (let i = 0; i < data.length; i++) {
            if (this.visited[i]) {
                continue;
            }
            this.visited[i] = true;
            const neighbors = this.rangeQuery(i, epsilon, distance);
            if (neighbors.length >= minPoints) {
                this.assigned[i] = true;
                const expandedNeighborhood = this.expandNeighborhood(neighbors, minPoints, epsilon, distance);
                this.clusters.push([i, ...expandedNeighborhood]);
            }
        }
        return {
            clusters: this.clusters,
            noise: this.points.filter((value, key) => !this.assigned[key]),
        };
    }
    expandNeighborhood(neighbors, minPoints, epsilon, distance) {
        const cluster = [];
        for (let i = 0; i < neighbors.length; i++) {
            const point = neighbors[i];
            if (!this.visited[point]) {
                this.visited[point] = true;
                const expandedNeighborhood = this.rangeQuery(point, epsilon, distance);
                if (expandedNeighborhood.length >= minPoints) {
                    neighbors = (0, lodash_1.union)(neighbors, expandedNeighborhood);
                }
            }
            if (!this.assigned[point]) {
                this.assigned[point] = true;
                cluster.push(point);
            }
        }
        return cluster;
    }
    rangeQuery(point, epsilon, distance) {
        const neighbors = [];
        for (let i = 0; i < this.points.length; i++) {
            if (point != i &&
                distance(this.points[i], this.points[point]) < epsilon) {
                neighbors.push(i);
            }
        }
        return neighbors;
    }
}
exports.DBSCAN = DBSCAN;
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