我需要聚类客户数据，包含分类和数字特征。数字特征不在同一范围内(年龄、收入……)。在我用标准刻度缩放后，我尝试了Mclust来获取数值数据，但这给了我交叉的组。在

1-如果标准量表的结果不令人满意，我是否应该标准化？

2-K-Prototype集群的最佳方式是什么？

3-聚类方法是否应该依赖于数据分布？在

我用熊猫

我用的是：#K-mean Cluster#search K

from scipy.spatial import distance as sci_distance

from sklearn import cluster as sk_cluster

cdata = data

K = range(1, 10)

KM = (sk_cluster.KMeans(n_clusters=k).fit(cdata) for k in K)

centroids = (k.cluster_centers_ for k in KM)

D_k = (sci_distance.cdist(cdata, cent, 'euclidean') for cent in centroids)

dist = (np.min(D, axis=1) for D in D_k)

avgWithinSS = [sum(d) / cdata.shape[0] for d in dist]

plt.plot(K, avgWithinSS, 'b*-')

plt.grid(True)

plt.xlabel('Number of clusters')

plt.ylabel('Average within-cluster sum of squares')

plt.title('Elbow for KMeans clustering')

plt.show()

#KMean Cluster

from sklearn.cluster import KMeans, AgglomerativeClustering,

AffinityPropagation #For clustering

from sklearn.mixture import GaussianMixture #For GMM clustering

import matplotlib.pyplot as plt # For graphics

import seaborn as sns

#Clustering

def doKmeans(X, nclust=3):

model = KMeans(nclust)

model.fit(X)

clust_labels = model.predict(X)

cent = model.cluster_centers_

return (clust_labels, cent)

clust_labels, cent = doKmeans(data, 3)

kmeans = pd.DataFrame(clust_labels)

data.insert((data.shape[1]),'kmeans',kmeans)

#Plot the clusters obtained using k means

fig = plt.figure()

ax = fig.add_subplot(111)

scatter = ax.scatter(data['var1'],data['var2'],

c=kmeans[0],s=50)

ax.set_title('K-Means Clustering')

ax.set_xlabel('var1')

ax.set_ylabel('var2')

plt.colorbar(scatter)