K-mean clustering 실습
from sklearn import cluster
from sklearn import datasets
import matplotlib.pyplot as plt
iris = datasets.load_iris()
data = iris['data']
#model = cluster.KMeans( n_clusters = 5 )
model = cluster.AffinityPropagation()
model.fit(data)
print(model.labels_)
labels = model.labels_
ldata = data[labels == 0]
plt.scatter(ldata[:,2], ldata[:,3], c='black', alpha=0.3, s=100, marker="o")
ldata = data[labels == 1]
plt.scatter(ldata[:,2], ldata[:,3], c='black', alpha=0.3, s=100, marker="^")
ldata = data[labels == 2]
plt.scatter(ldata[:,2], ldata[:,3], c='black', alpha=0.3, s=100, marker="*")
ldata = data[labels == 3]
plt.scatter(ldata[:,2], ldata[:,3], c='black', alpha=0.3, s=100, marker="1")
ldata = data[labels == 4]
plt.scatter(ldata[:,2], ldata[:,3], c='black', alpha=0.3, s=100, marker="+")
ldata = data[labels == 5]
plt.scatter(ldata[:,2], ldata[:,3], c='black', alpha=0.3, s=100, marker="P")
ldata = data[labels == 6]
plt.scatter(ldata[:,2], ldata[:,3], c='black', alpha=0.3, s=100, marker="x")
plt.xlabel(iris['feature_names'][2], fontsize='large')
plt.ylabel(iris['feature_names'][3], fontsize='large')
plt.show()
