Rule-based Approach

A rule-based approach in anomaly detection involves defining a set of rules or criteria to identify anomalies in a dataset. These rules are typically based on domain knowledge or heuristics and are used to flag data points that deviate from expected patterns or behavior.

Rule-based methods are intuitive and can be effective in scenarios where anomalies are well-understood and can be captured by explicit rules.

Example

Let's look at the following code that represents rule-based approach:

python
Kopier kode
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# Define a rule-based anomaly detection function
def rule_based_anomaly_detection(data, threshold=3):
    mean = np.mean(data)
    std_dev = np.std(data)
    
    anomalies = []
    for i, value in enumerate(data):
        if abs(value - mean) > threshold * std_dev:
            anomalies.append((i, value))
    
    return anomalies

# Set the anomaly detection threshold
threshold = 3

# Detect anomalies in the dataset
anomalies = rule_based_anomaly_detection(data, threshold)

# Print the detected anomalies
print("Detected anomalies:")
for index, value in anomalies:
    print(f"Index {index}: Value {value}")

We define the rule_based_anomaly_detection() function, which checks each data point against the defined rule based on a threshold (in this case, three times the standard deviation from the mean). Finally, we detect and print the anomalies in the dataset.

Note

In this example, we employed statistical concepts such as mean and standard deviation. However, it's essential to clarify that this approach is rule-based rather than strictly statistical. This distinction arises because, in a rule-based approach, we manually set the threshold values ourselves, whereas statistical methods typically rely on predefined thresholds.