/**
 * Choose K different random points from the original data
 * @ignore
 * @param data - Points in the format to cluster [x,y,z,...]
 * @param K - number of clusters
 * @param seed - seed for random number generation
 * @returns - Initial random points
 */
export declare function random(data: number[][], K: number, seed?: number): number[][];
/**
 * Chooses the most distant points to a first random pick
 * @ignore
 * @param data - Points in the format to cluster [x,y,z,...]
 * @param K - number of clusters
 * @param distanceMatrix - matrix with the distance values
 * @param seed - seed for random number generation
 * @returns - Initial random points
 */
export declare function mostDistant(data: number[][], K: number, distanceMatrix: number[][], seed?: number): number[][];
interface Options {
    seed: number;
    localTrials: number;
}
/**
 * Chooses the initial centers using the kmeans++ method.
 * Implementation inspired from scikit.
 * @ignore
 * @param X - Points in the format to cluster [x,y,z,...].
 * @param K - Number of clusters.
 * @param options - Options for the kmeans++ initialization.
 * @returns Initial centers in format [x,y,z,...].
 */
export declare function kmeanspp(X: number[][], K: number, options?: Partial<Options>): number[][];
export {};
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