给我一个KMeans原数据集，再给我

一个KMeans原数据集：

[
  [2.0, 3.0],
  [2.5, 5.0],
  [1.0, 1.5],
  [5.5, 6.5],
  [7.0, 7.5],
  [6.0, 5.0],
  [4.5, 7.0],
  [3.5, 4.5],
  [2.5, 4.5],
  [5.5, 8.0],
  [5.0, 5.5],
  [5.0, 7.0],
  [4.0, 5.0],
  [6.5, 6.0]
]

再给你一个KMeans的代码实现，你可以使用以下Python代码实现KMeans算法：

import random
from typing import List, Tuple

def distance(p1: Tuple[float, float], p2: Tuple[float, float]) -> float:
    return ((p1[0] - p2[0]) ** 2 + (p1[1] - p2[1]) ** 2) ** 0.5

def kmeans(data: List[Tuple[float, float]], k: int) -> Tuple[List[List[Tuple[float, float]]], List[Tuple[float, float]]]:
    centroids = random.sample(data, k)
    assignments = [-1] * len(data)
    while True:
        new_assignments = []
        for point in data:
            distances = [distance(point, centroid) for centroid in centroids]
            closest_centroid = distances.index(min(distances))
            new_assignments.append(closest_centroid)
        if new_assignments == assignments:
            break
        assignments = new_assignments
        for i in range(k):
            assigned_points = [data[j] for j in range(len(data)) if assignments[j] == i]
            centroid = [sum(point[0] for point in assigned_points) / len(assigned_points),
                        sum(point[1] for point in assigned_points) / len(assigned_points)]
            centroids[i] = centroid
    clusters = [[] for _ in range(k)]
    for i, point in enumerate(data):
        clusters[assignments[i]].append(point)
    return clusters, centroids

你可以使用以上代码对数据集进行聚类，函数`kmeans(data, k)`的第一个参数`data`需要传入数据集，第二个参数`k`是聚类的数量。