python - 如何在 Kmeans 中获得中心点

问题描述

以下是我正在使用的数据集示例：

   id,product,store,revenue,store_capacity,state
    1,Ball,AB,222,1000,CA
    1,Pen,AB,234,1452,WD
    2,Books,CD,543,888,MA
    2,Ink,EF,123,9865,NY

代码如下

import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
from sklearn.cluster import KMeans
from scipy.spatial.distance import euclidean
import seaborn as sns
from sklearn.preprocessing import MinMaxScaler, StandardScaler
sns.set(rc={'figure.figsize':(11.7,8.27)})

df = pd.read_csv(r'1.csv',index_col=None)
dummies = pd.get_dummies(data = df)
km = KMeans(n_clusters=2).fit(dummies)
labels = km.predict(dummies)
dummies['cluster_id'] = km.labels_
def distance_to_centroid(row, centroid):
    row = row[['id', 'product', 'store', 'revenue','store_capacity', 'state_AL', 'state_CA', 'state_CH',
       'state_WD', 'country_India', 'country_Japan', 'country_USA']]
    return euclidean(row, centroid)
dummies['distance_to_center0'] = dummies.apply(lambda r: distance_to_centroid(r,
    km.cluster_centers_[0]),1)

dummies['distance_to_center1'] = dummies.apply(lambda r: distance_to_centroid(r,
    km.cluster_centers_[1]),1)

dummies['distance_to_center2'] = dummies.apply(lambda r: distance_to_centroid(r,
    km.cluster_centers_[2]),1)

dummies_df = dummies[['distance_to_center0','distance_to_center1','cluster_id']]
test = {0:"Blue", 1:"Red", 2:"Green"}
sns.scatterplot(x="distance_to_center0", y="distance_to_center1", data=dummies_df, hue="cluster_id", palette = test)

我需要获取每个集群的中心，下面的代码正在获取centroid of each element意味着从每个元素到集群中心点的距离是多少

centroids  = km.cluster_centers_
centroid_labels = [centroids[i] for i in labels]
centroid_label

我想得到每个集群的中心点

标签： pythonmachine-learningk-meanscentroid

python - 如何在 Kmeans 中获得中心点

问题描述

解决方案

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