/** @import { InputType } from "../index.js" */
/** @import { ParametersCURE } from "./index.js" */
/**
 * CURE (Clustering Using REpresentatives)
 *
 * An efficient clustering algorithm for large databases that is robust to outliers
 * and identifies clusters with non-spherical shapes and wide variances in size.
 *
 * @class
 * @extends Clustering<ParametersCURE>
 * @category Clustering
 */
export class CURE extends Clustering<ParametersCURE> {
  /**
   * @param {InputType} points
   * @param {Partial<ParametersCURE>} parameters
   */
  constructor(points: InputType, parameters?: Partial<ParametersCURE>);
  /** @type {number} */
  _K: number;
  /** @type {number} */
  _num_representatives: number;
  /** @type {number} */
  _shrink_factor: number;
  /**
   * @private
   * @type {CURECluster[]}
   */
  private _clusters;
  /** @type {number[]} */
  _cluster_ids: number[];
  /**
   * Initialize each point as its own cluster
   * @private
   */
  private _initialize_clusters;
  /**
   * Compute distance between two clusters using representative points
   * @private
   * @param {CURECluster} cluster1
   * @param {CURECluster} cluster2
   * @returns {number}
   */
  private _cluster_distance;
  /**
   * Find the closest pair of clusters
   * @private
   * @returns {[number, number, number]} [index1, index2, distance]
   */
  private _find_closest_clusters;
  /**
   * Merge two clusters
   * @private
   * @param {CURECluster} cluster1
   * @param {CURECluster} cluster2
   * @returns {CURECluster}
   */
  private _merge_clusters;
  /**
   * Run CURE clustering algorithm
   * @private
   */
  private _cure;
  /**
   * Build the cluster list (point -> cluster assignment)
   * @private
   */
  private _build_cluster_ids;
  /**
   * @returns {number[][]}
   */
  get_clusters(): number[][];
  /**
   * @returns {number[]}
   */
  get_cluster_list(): number[];
}
import type { ParametersCURE } from "./index.js";
import { Clustering } from "./Clustering.js";
import type { InputType } from "../index.js";
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