export { CURE } from "./CURE.js";
export { HierarchicalClustering } from "./Hierarchical_Clustering.js";
export { KMeans } from "./KMeans.js";
export { KMedoids } from "./KMedoids.js";
export { MeanShift } from "./MeanShift.js";
export { OPTICS } from "./OPTICS.js";
export { XMeans } from "./XMeans.js";
export type ParametersHierarchicalClustering = {
  linkage: "single" | "complete" | "average";
  metric: Metric | "precomputed";
};
export type ParametersKMeans = {
  K: number;
  /**
   * Default is `euclidean`
   */
  metric: Metric;
  /**
   * Default is `1212`
   */
  seed: number;
  /**
   * - Initial centroids. Default is `null`
   */
  initial_centroids?: Float64Array<ArrayBufferLike>[] | number[][] | undefined;
};
export type ParametersKMedoids = {
  /**
   * - Number of clusters
   */
  K: number;
  /**
   * - Maximum number of iterations. Default is 10 * Math.log10(N). Default is `null`
   */
  max_iter: number | null;
  /**
   * - Metric defining the dissimilarity. Default is `euclidean`
   */
  metric: Metric;
  /**
   * - Seed value for random number generator. Default is `1212`
   */
  seed: number;
};
export type ParametersOptics = {
  /**
   * - The minimum distance which defines whether a point is a neighbor or not.
   */
  epsilon: number;
  /**
   * - The minimum number of points which a point needs to create a cluster. (Should be higher than 1, else each point creates a cluster.)
   */
  min_points: number;
  /**
   * - The distance metric which defines the distance between two points of the points. Default is `euclidean`
   */
  metric: Metric;
};
export type ParametersXMeans = {
  /**
   * - Minimum number of clusters. Default is `2`
   */
  K_min: number;
  /**
   * - Maximum number of clusters. Default is `10`
   */
  K_max: number;
  /**
   * - Distance metric function. Default is `euclidean`
   */
  metric: Metric;
  /**
   * - Random seed. Default is `1212`
   */
  seed: number;
  /**
   * - Minimum points required to consider splitting a cluster. Default is `25`
   */
  min_cluster_size: number;
  /**
   * - Convergence tolerance for KMeans. Default is `0.001`
   */
  tolerance: number;
};
export type ParametersMeanShift = {
  /**
   * - bandwidth
   */
  bandwidth: number;
  /**
   * - Metric defining the dissimilarity. Default is `euclidean`
   */
  metric: Metric;
  /**
   * - Seed value for random number generator. Default is `1212`
   */
  seed: number;
  /**
   * - Kernel function. Default is `gaussian`
   */
  kernel: "flat" | "gaussian" | ((dist: number) => number);
  /**
   * - Maximum number of iterations. Default is `Math.max(10, Math.floor(10 * Math.log10(N)))`
   */
  max_iter?: number | undefined;
  /**
   * - Convergence tolerance. Default is `1e-3`
   */
  tolerance?: number | undefined;
};
export type ParametersCURE = {
  /**
   * - Target number of clusters. Default is `2`
   */
  K: number;
  /**
   * - Number of representative points per cluster. Default is `5`
   */
  num_representatives: number;
  /**
   * - Factor to shrink representatives toward centroid (0-1). Default is `0.5`
   */
  shrink_factor: number;
  /**
   * - Distance metric function. Default is `euclidean`
   */
  metric: Metric;
  /**
   * - Random seed. Default is `1212`
   */
  seed: number;
};
import type { Metric } from "../metrics/index.js";
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