The function outlier_detection_kmeans in the hana_ml.algorithms.pal.clustering module uses the K-means algorithm to detect outliers in a given dataset by finding the farthest point from the centroid, with various parameters to customize the process such as the number of clusters, distance type, proportion of outliers, and more.
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Here is the executable code template for the function `outlier_detection_kmeans`:

```python
from hana_ml.algorithms.pal.clustering import outlier_detection_kmeans

# Assuming that `df` is your DataFrame
outliers, stats, centers = outlier_detection_kmeans(
    data=df, 
    key='ID',
    features=None, 
    n_clusters=None, 
    distance_level='euclidean', 
    contamination=0.15, 
    sum_distance=True, 
    init=None, 
    max_iter=None, 
    normalization=None, 
    tol=None, 
    distance_threshold=3, 
    thread_number=None
)

# To view the results
print(outliers.collect())
print(stats.collect())
print(centers.collect())
```

Please replace `df` with your actual DataFrame. You can also adjust the parameters according to your needs.