export interface CentroidWithInformation {
    centroid: number[];
    error: number;
    size: number;
}
export declare class KMeansResult {
    /**
     * Result of the kmeans algorithm
     * @param clusters - the cluster identifier for each data dot
     * @param centroids - the K centers in format [x,y,z,...], the error and size of the cluster
     * @param converged - Converge criteria satisfied
     * @param iterations - Current number of iterations
     * @param distance - Distance function to use between the points
     * @class
     */
    clusters: number[];
    centroids: number[][];
    converged: boolean;
    iterations: number;
    distance: (a: number[], b: number[]) => number;
    constructor(clusters: number[], centroids: number[][], converged: boolean, iterations: number, distance: (a: number[], b: number[]) => number);
    /**
     * Allows to compute for a new array of points their cluster id
     * @param data - the [x,y,z,...] points to cluster
     * @returns - cluster id for each point
     */
    nearest(data: number[][]): number[];
    /**
     * Returns the centroid, mean error, and size of each cluster.
     * The error of an empty cluster is `-1`.
     * @ignore
     * @param data - the [x,y,z,...] points to cluster
     * @returns for each cluster, its centroid, mean error, and size.
     */
    computeInformation(data: number[][]): CentroidWithInformation[];
}
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