DPK-means算法 python

DPK-means算法是一种基于密度的聚类算法，可以用于发现数据中的簇结构。以下是DPK-means算法的Python代码实现：

import numpy as np
from sklearn.cluster import KMeans

def dpkmeans(X, k, alpha):
    n = len(X)
    d = len(X[0])
    centroids = [X[0]]
    clusters = [0] * n
    for i in range(1, n):
        dists = [np.linalg.norm(X[i] - c) for c in centroids]
        if min(dists) >= alpha:
            centroids.append(X[i])
            clusters[i] = len(centroids) - 1
        else:
            clusters[i] = dists.index(min(dists))
    while len(centroids) < k:
        max_dist = 0
        max_i = None
        for i in range(n):
            if clusters[i] == -1:
                dists = [np.linalg.norm(X[i] - c) for c in centroids]
                if min(dists) > max_dist:
                    max_dist = min(dists)
                    max_i = i
        if max_i is not None:
            centroids.append(X[max_i])
            clusters[max_i] = len(centroids) - 1
    kmeans = KMeans(n_clusters=k, init=np.array(centroids))
    kmeans.fit(X)
    return kmeans.labels_

其中，参数X是一个n x d的矩阵，表示n个d维数据点；k是簇的个数；alpha是一个控制簇密度的阈值。函数返回一个长度为n的列表，表示每个数据点所属的簇的标签。