【阿旭机器学习实战】【18】KMeans聚类中的常见问题

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本文对机器学习中的KMeans聚类中常见的几个问题进行说明介绍。

KMeans聚类中的常见问题

使用make_blobs创建样本点

samples,targets = datasets.make_blobs(n_samples=150,n_features=2,centers=3,random_state=1)

plt.scatter(samples[:,0],samples[:,1],c=targets)

1、K值不合适

km = KMeans(n_clusters=2)

km.fit(samples)

KMeans(algorithm='auto', copy_x=True, init='k-means++', max_iter=300,
    n_clusters=2, n_init=10, n_jobs=1, precompute_distances='auto',
    random_state=None, tol=0.0001, verbose=0)

y_ = km.predict(samples)

metrics.adjusted_rand_score(targets,y_)

0.5681159420289855

metrics.silhouette_score(samples,km.labels_)

0.7802809392385796

一般综合考虑ARI指标和轮廓系数来决定划分几个聚类。

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2、数据偏差较大

samples,target = datasets.make_blobs(n_features=2,n_samples=150,centers=3,random_state=5)

plt.scatter(samples[:,0],samples[:,1],c=target)

km = KMeans(n_clusters=2)

y_ = km.fit_predict(samples)

metrics.adjusted_rand_score(y_,target)

0.5681159420289855

metrics.silhouette_score(samples,km.labels_)

0.7744057193895231

对于偏差比较大的数据，可以引入一个修正矩阵来对特征进行修正

trans = np.array([[0.6,-0.6],[-0.4,0.8]])

sam1 = np.dot(samples,trans)

plt.scatter(sam1[:,0],sam1[:,1],c=target)

km = KMeans(n_clusters=2)

y_ = km.fit_predict(sam1)

metrics.adjusted_rand_score(y_,target)

0.5681159420289855

metrics.silhouette_score(sam1,km.labels_)

0.8527525114558019

3、各个类别内部数据的标准差差别很大

samples,target= datasets.make_blobs(n_features=2,n_samples=150,centers=3,
                                    cluster_std=[0.5,2,10])

plt.scatter(samples[:,0],samples[:,1],c=target)

km = KMeans(n_clusters=3)

y_ = km.fit_predict(samples)

plt.scatter(samples[:,0],samples[:,1],c=y_)

metrics.adjusted_rand_score(y_,target)

0.530328438685834

metrics.silhouette_score(samples,km.labels_)

0.5932400629187524

4、样本量差别很大

samples,target = datasets.make_blobs(n_samples=1500,n_features=2,centers=3,random_state=0)

train1 = samples[target==0][:10]
train2 = samples[target==1][:100]
train3 = samples[target==2][:500]
train = np.concatenate([train1,train2,train3])

# 用train作为训练数据
train.shape

(610, 2)

# 生成标签
target = [0]*10 + [1]*100 + [2]*500

plt.scatter(train[:,0],train[:,1],c=target)

km = KMeans(n_clusters=3)

y_ = km.fit_predict(train)

plt.scatter(train[:,0],train[:,1],c=y_)

metrics.adjusted_rand_score(y_,target)

0.3479149392627809

metrics.silhouette_score(train,km.labels_)

0.35994070955867

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