/**
 * Adds clusters to the given data points based on the specified distance and minimum number of neighbours.
 *
 * This function is an implementation of the [DBSCAN](https://en.wikipedia.org/wiki/DBSCAN), a density-based clustering algorithm to group points into clusters. Points are classified as core points, border points, or noise based on their neighbourhood.
 *
 * - Core points have at least `minNeighbours` (including themselves) within `minDistance`.
 * - Border points are within `minDistance` of a core point but have fewer than `minNeighbours`.
 * - Noise points are not within `minDistance` of any core points.
 *
 * The function modifies the input data array in place, adding `clusterId` and `clusterType` properties to each point.
 *
 * @example
 * Basic usage
 * ```js
 * const data = [
 *   { x: 1, y: 2 },
 *   { x: 2, y: 3 },
 *   { x: 10, y: 10 },
 *   { x: 11, y: 11 },
 *   { x: 50, y: 50 }
 * ];
 *
 * // We use the euclideanDistance function from the journalism library to calculate the distance between points.
 * const distance = (a, b) => euclideanDistance(a.x, a.y, b.x, b.y);
 *
 * addClusters(data, 5, 2, distance);
 *
 * console.log(data);
 * // Output:
 * // [
 * //   { x: 1, y: 2, clusterId: 'cluster1', clusterType: 'core' },
 * //   { x: 2, y: 3, clusterId: 'cluster1', clusterType: 'core' },
 * //   { x: 10, y: 10, clusterId: 'cluster2', clusterType: 'core' },
 * //   { x: 11, y: 11, clusterId: 'cluster2', clusterType: 'core' },
 * //   { x: 50, y: 50, clusterId: null, clusterType: 'noise' }
 * // ]
 * ```
 *
 * @param data - An array of data points where each point is an object with arbitrary properties.
 * @param minDistance - The maximum distance between points to be considered neighbours.
 * @param minNeighbours - The minimum number of neighbours (including the current point) required for a point to be considered a core point.
 * @param distance - A function that calculates the distance between two points.
 * @param options - Optional settings for the clustering process.
 * @param options.reset - If true, resets the clusterId and clusterType of all points before clustering.
 *
 * @category Statistics
 */
export default function addClusters(data: {
    [key: string]: unknown;
}[], minDistance: number, minNeighbours: number, distance: (a: {
    [key: string]: unknown;
}, b: {
    [key: string]: unknown;
}) => number, options?: {
    reset?: boolean;
}): void;