其他聚类欺诈检测方法

Python 中的欺诈检测

Charlotte Werger

Data Scientist

聚类方法多种多样

标记欺诈的不同方式：用最小簇

实际更接近这样

DBSCAN 与 K-means 对比

无需预设簇数
可调簇内点的最大距离
可设簇的最小样本数
非规则形状数据表现更好
但计算开销更高

实现 DBSCAN

from sklearn.cluster import DBSCAN
db = DBSCAN(eps=0.5, min_samples=10, n_jobs=-1).fit(X_scaled)

# Get the cluster labels (aka numbers)
pred_labels = db.labels_

# Count the total number of clusters
n_clusters_ = len(set(pred_labels)) - (1 if -1 in pred_labels else 0)
# Print model results
print('Estimated number of clusters: %d' % n_clusters_)

Estimated number of clusters: 31

检查簇大小

# Print model results
print("Silhouette Coefficient: %0.3f" % metrics.silhouette_score(X_scaled, pred_labels))

Silhouette Coefficient: 0.359

# Get sample counts in each cluster 
counts = np.bincount(pred_labels[pred_labels>=0])
print (counts)

[ 763  496  840  355 1086  676   63  306  560  134   28   18  262  128  332  22  
   22   13   31   38   36   28   14   12   30   10   11   10   21   10    5]

Passons à la pratique !

Python 中的欺诈检测