/**
 * Result of `kmeans`: the cluster label of each row, the centroids, and the total
 * within-cluster sum of squares (`inertia`).
 */
export interface KMeansResult {
    labels: number[];
    centroids: number[][];
    inertia: number;
    /** Number of Lloyd iterations actually run. */
    iterations: number;
    /** True if the assignment stabilized before `maxIter` (false = hit the cap). */
    converged: boolean;
}
/**
 * k-means clustering (Lloyd's algorithm) of row-vectors `data` into `k` clusters.
 * Deterministic maximin seeding (farthest-point), so repeated runs agree. Returns
 * cluster `labels`, `centroids`, and the total within-cluster sum of squares (`inertia`).
 */
export declare function kmeans(data: readonly number[][], k: number, opts?: {
    maxIter?: number;
}): KMeansResult;
/**
 * Spectral clustering of a weighted graph given by its adjacency matrix into `k`
 * clusters. Embeds nodes via the `k` smallest eigenvectors of the symmetric
 * normalized Laplacian (reusing `laplacianMatrix` + `eigs`), then k-means on the
 * embedding. Returns a label per node.
 */
export declare function spectralClustering(adjacency: readonly number[][], k: number, opts?: {
    maxIter?: number;
}): number[];
//# sourceMappingURL=clustering-extra.d.ts.map