/** @import {InputType} from "../index.js" */
/** @import { ParametersKMedoids } from "./index.js" */
/**
 * K-Medoids (PAM - Partitioning Around Medoids)
 *
 * A robust clustering algorithm similar to K-Means, but uses actual data points (medoids)
 * as cluster centers and can work with any distance metric.
 *
 * @class
 * @extends Clustering<ParametersKMedoids>
 * @category Clustering
 * @see {@link KMeans} for a faster but less robust alternative
 */
export class KMedoids extends Clustering<ParametersKMedoids> {
  /**
   * @param {InputType} points - Data matrix
   * @param {Partial<ParametersKMedoids>} parameters
   * @see {@link https://link.springer.com/chapter/10.1007/978-3-030-32047-8_16} Faster k-Medoids Clustering: Improving the PAM, CLARA, and CLARANS Algorithms
   */
  constructor(points: InputType, parameters?: Partial<ParametersKMedoids>);
  _A: Float64Array<ArrayBufferLike>[];
  _max_iter: number;
  _distance_matrix: Matrix;
  _randomizer: Randomizer;
  _clusters: any[];
  _cluster_medoids: number[];
  _is_initialized: boolean;
  /** @returns {number[]} The cluster list */
  get_cluster_list(): number[];
  /** @returns {number[][]} - Array of clusters with the indices of the rows in given points. */
  get_clusters(): number[][];
  /** @returns {number} */
  get k(): number;
  /** @returns {number[]} */
  get medoids(): number[];
  /** @returns {number[]} */
  get_medoids(): number[];
  generator(): AsyncGenerator<number[][], void, unknown>;
  /** Algorithm 1. FastPAM1: Improved SWAP algorithm */
  /** FastPAM1: One best swap per iteration */
  _iteration(): boolean;
  /**
   *
   * @param {number} i
   * @param {number} j
   * @param {Float64Array?} x_i
   * @param {Float64Array?} x_j
   * @returns
   */
  _get_distance(i: number, j: number, x_i?: Float64Array | null, x_j?: Float64Array | null): number;
  /**
   *
   * @param {Float64Array} x_j
   * @param {number} j
   * @returns
   */
  _nearest_medoid(
    x_j: Float64Array,
    j: number,
  ): {
    distance_nearest: number;
    index_nearest: number;
    distance_second: number;
    index_second: number;
  };
  _update_clusters(): void;
  /**
   * Computes `K` clusters out of the `matrix`.
   * @param {number} K - Number of clusters.
   * @param {number[]} cluster_medoids
   */
  init(K: number, cluster_medoids: number[]): this;
  /**
   * Algorithm 3. FastPAM LAB: Linear Approximate BUILD initialization.
   *
   * @param {number} K - Number of clusters
   * @returns {number[]}
   */
  _get_random_medoids(K: number): number[];
}
import type { ParametersKMedoids } from "./index.js";
import { Clustering } from "./Clustering.js";
import { Matrix } from "../matrix/index.js";
import { Randomizer } from "../util/index.js";
import type { InputType } from "../index.js";
//# sourceMappingURL=KMedoids.d.ts.map