<과목> 기계 학습

제6장: 알고리즘 2(k-means)

k-평균법(k-means)이란?

k-평균법(k-means) 알고리즘

(연습) k-means (k-means)를 사용하여 3 개의 클러스터로 분류

#https://datahexa.com/kmeans-clustering-with-wine-dataset/参考
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
from sklearn import cluster, preprocessing, datasets

from sklearn.cluster import KMeans

wine = datasets.load_wine()
X = wine.data
X.shape

y=wine.target
y.shape

(178,)

wine.target_names

array(['class_0', 'class_1', 'class_2'], dtype='<U7')

model = KMeans(n_clusters=3)
labels = model.fit_predict(X)
df = pd.DataFrame({'labels': labels})
type(df)

pandas.core.frame.DataFrame

def species_label(theta):
    if theta == 0:
        return wine.target_names[0]
    if theta == 1:
        return wine.target_names[1]
    if theta == 2:
        return wine.target_names[2]
df['species'] = [species_label(theta) for theta in wine.target]
pd.crosstab(df['labels'], df['species'])