DBSCAN聚类算法（商场数据分析）

论搬砖的艺术

553人浏览 · 2023-02-25 22:35:18

论搬砖的艺术 · 2023-02-25 22:35:18 发布

DBSCAN聚类算法（商场数据分析）

引入库
邻域半径，最少点数目参数
数据
最佳参数分析
选择最佳参数，查看结果

引入库

from sklearn.cluster import DBSCAN

邻域半径，最少点数目参数

from itertools import product

eps_values = np.arange(8,12.75,0.25) # eps values to be investigated
min_samples = np.arange(3,10) # min_samples values to be investigated

DBSCAN_params = list(product(eps_values, min_samples))

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数据

X_numerics = mall_data[['Age', 'Annual Income (k$)', 'Spending Score (1-100)']] # subset with numeric variables only

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最佳参数分析

from sklearn.metrics import silhouette_score

no_of_clusters = []
sil_score = []

for p in DBSCAN_params:
    DBS_clustering = DBSCAN(eps=p[0], min_samples=p[1]).fit(X_numerics)
    no_of_clusters.append(len(np.unique(DBS_clustering.labels_)))
    sil_score.append(silhouette_score(X_numerics, DBS_clustering.labels_))
    #print(DBS_clustering.labels_)

tmp = pd.DataFrame.from_records(DBSCAN_params, columns =['Eps', 'Min_samples'])   
tmp['No_of_clusters'] = no_of_clusters

pivot_1 = pd.pivot_table(tmp, values='No_of_clusters', index='Min_samples', columns='Eps')

fig, ax = plt.subplots(figsize=(12,6))
sns.heatmap(pivot_1, annot=True,annot_kws={"size": 16}, cmap="YlGnBu", ax=ax)
ax.set_title('Number of clusters')
plt.show()

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tmp = pd.DataFrame.from_records(DBSCAN_params, columns =['Eps', 'Min_samples'])   
tmp['Sil_score'] = sil_score

pivot_1 = pd.pivot_table(tmp, values='Sil_score', index='Min_samples', columns='Eps')

fig, ax = plt.subplots(figsize=(18,6))
sns.heatmap(pivot_1, annot=True, annot_kws={"size": 10}, cmap="YlGnBu", ax=ax)
plt.show()

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选择最佳参数，查看结果

DBS_clustering = DBSCAN(eps=12.5, min_samples=4).fit(X_numerics)

DBSCAN_clustered = X_numerics.copy()
DBSCAN_clustered.loc[:,'Cluster'] = DBS_clustering.labels_ # append labels to points

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DBSCAN_clust_sizes = DBSCAN_clustered.groupby('Cluster').size().to_frame()  # 统计某一类的种类个数，返回dataframe
DBSCAN_clust_sizes.columns = ["DBSCAN_size"]
DBSCAN_clust_sizes

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画图分析

outliers = DBSCAN_clustered[DBSCAN_clustered['Cluster']==-1]

fig2, (axes) = plt.subplots(1,2,figsize=(12,5))


sns.scatterplot('Annual Income (k$)', 'Spending Score (1-100)',
                data=DBSCAN_clustered[DBSCAN_clustered['Cluster']!=-1],
                hue='Cluster', ax=axes[0], palette='Set1', legend='full', s=45)

sns.scatterplot('Age', 'Spending Score (1-100)',
                data=DBSCAN_clustered[DBSCAN_clustered['Cluster']!=-1],
                hue='Cluster', palette='Set1', ax=axes[1], legend='full', s=45)

axes[0].scatter(outliers['Annual Income (k$)'], outliers['Spending Score (1-100)'], s=5, label='outliers', c="k")
axes[1].scatter(outliers['Age'], outliers['Spending Score (1-100)'], s=5, label='outliers', c="k")
axes[0].legend()
axes[1].legend()

plt.setp(axes[0].get_legend().get_texts(), fontsize='10')
plt.setp(axes[1].get_legend().get_texts(), fontsize='10')

plt.show()