머신러닝 - Hierarchical Clustering

Hierarchical Clustering

Library 임포트

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

dataset 읽어오기

df = pd.read_csv('Mall_Customers.csv')

X = df.iloc[:,3:]

X.head(3)

	Annual Income (k$)	Spending Score (1-100)
0	15	39
1	15	81
2	16	6

Dendrogram 을 그리고, 최적의 클러스터 갯수를 찾아보자.

import scipy.cluster.hierarchy as sch

sch.dendrogram(sch.linkage(X,method='ward'))
plt.title('Dendrogram')
plt.xlabel('Customers')
plt.ylabel('Euclidean distance')
plt.show()

Training the Hierarchical Clustering model

from sklearn.cluster import AgglomerativeClustering

hc = AgglomerativeClustering(n_clusters=5)

y_pred = hc.fit_predict(X)

df['Group'] = y_pred

df

	CustomerID	Genre	Age	Annual Income (k$)	Spending Score (1-100)	Group
0	1	Male	19	15	39	4
1	2	Male	21	15	81	3
2	3	Female	20	16	6	4
3	4	Female	23	16	77	3
4	5	Female	31	17	40	4
...	...	...	...	...	...	...
195	196	Female	35	120	79	2
196	197	Female	45	126	28	0
197	198	Male	32	126	74	2
198	199	Male	32	137	18	0
199	200	Male	30	137	83	2

200 rows × 6 columns

그루핑 정보를 확인

plt.scatter(X.values[y_pred == 0, 0], X.values[y_pred == 0, 1], s = 100, c = 'red', label = 'Cluster 1')
plt.scatter(X.values[y_pred == 1, 0], X.values[y_pred == 1, 1], s = 100, c = 'blue', label = 'Cluster 2')
plt.scatter(X.values[y_pred == 2, 0], X.values[y_pred == 2, 1], s = 100, c = 'green', label = 'Cluster 3')
plt.scatter(X.values[y_pred == 3, 0], X.values[y_pred == 3, 1], s = 100, c = 'cyan', label = 'Cluster 4')
plt.scatter(X.values[y_pred == 4, 0], X.values[y_pred == 4, 1], s = 100, c = 'magenta', label = 'Cluster 5')
plt.title('Clusters of customers')
plt.xlabel('Annual Income (k$)')
plt.ylabel('Spending Score (1-100)')
plt.legend()
plt.show()