What does the following code perform in terms of anomaly detection?

python

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from sklearn.cluster import KMeans

import numpy as np

data $=$ np$.$array$([[10],[12],[14],[13],[15],[100],[12],[13]])$

kmeans $=$ KMeans$($n$\_$clusters$=2)$

kmeans.fit$($data$)$

labels $=$ kmeans.labels$\_$

print$($labels$)$

Question $43$ options:

Classifies the data into two clusters

Detects anomalies based on distance from the cluster center

Normalizes the data

Reduces dimensionality of the data