请用python写一个dbscan三维聚类算法，其中聚类数据从excel中获取

好的，首先需要安装pandas和numpy库来读取excel文件和进行数值计算，同时还需要matplotlib库来可视化结果。代码如下：

import pandas as pd
import numpy as np
import matplotlib.pyplot as plt

def dist(p1, p2):
    return np.sqrt(np.sum((p1 - p2) ** 2))

def dbscan(data, eps, min_samples):
    # 初始化核心点集合和边界点集合
    core_points = set()
    border_points = set()
    noise_points = set()

    # 计算点之间的距离矩阵
    dist_matrix = np.zeros((len(data), len(data)))
    for i in range(len(data)):
        for j in range(i+1, len(data)):
            d = dist(data[i], data[j])
            dist_matrix[i][j] = d
            dist_matrix[j][i] = d

    # 找到核心点和边界点
    for i in range(len(data)):
        if len([j for j in range(len(data)) if dist_matrix[i][j] <= eps]) >= min_samples:
            core_points.add(i)
        elif len([j for j in range(len(data)) if dist_matrix[i][j] <= eps and j in core_points]) > 0:
            border_points.add(i)
        else:
            noise_points.add(i)

    # 开始聚类
    clusters = []
    visited = set()
    for i in core_points:
        if i not in visited:
            cluster = set()
            visited.add(i)
            cluster.add(i)
            while len(cluster) > 0:
                j = cluster.pop()
                neighbors = set([k for k in range(len(data)) if dist_matrix[j][k] <= eps])
                visited.update(neighbors)
                if len(neighbors) >= min_samples:
                    cluster.update(neighbors)
            clusters.append(cluster)

    # 将边界点分配到聚类中
    for i in border_points:
        for j in range(len(clusters)):
            if len(clusters[j].intersection(set([i]))) > 0:
                clusters[j].add(i)
                break

    return clusters, list(noise_points)

# 从Excel中读取数据
df = pd.read_excel('data.xlsx')
data = df.to_numpy()

# 运行聚类算法
clusters, noise_points = dbscan(data, eps=3, min_samples=3)
print('聚类结果:', clusters)
print('噪声点:', noise_points)

# 可视化聚类结果
colors = ['r', 'g', 'b', 'c', 'm', 'y', 'k']
for i, cluster in enumerate(clusters):
    plt.scatter(data[list(cluster), 0], data[list(cluster), 1], c=colors[i%len(colors)])
plt.scatter(data[noise_points, 0], data[noise_points, 1], c='gray')
plt.show()

其中 `data.xlsx` 是包含三维数据的Excel文件，`eps` 是邻域半径，`min_samples` 是最小样本数。输出结果包括聚类结果和噪声点，同时还会将聚类结果可视化。