The AprioriLite class in the hana_ml.algorithms.pal.association module is a lightweight version of the Apriori algorithm for association rule mining, which reduces computational overhead by focusing on the creation and analysis of up to two-item sets, making it useful for large datasets.
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Here is a Python code template based on the provided help doc:

```python
from hana_ml.algorithms.pal.association import AprioriLite
from hana_ml import DataFrame

# Assuming that connection_context is the connection to the HANA system

# Create DataFrame for the input data
df = DataFrame(connection_context, 'SELECT * FROM YOUR_TABLE')

# Initialize a AprioriLite object and set its parameters
apl = AprioriLite(min_support=0.1,
                  min_confidence=0.3,
                  subsample=1.0,
                  recalculate=False,
                  timeout=3600,
                  pmml_export='single-row')

# Perform the fit() and obtain the result
apl.fit(data=df)

# Print the result
print(apl.result_.collect())
```

Please replace `'SELECT * FROM YOUR_TABLE'` with your actual SQL statement to select data from your HANA table.