As organizations collect larger data sets with potential insights into business activity, detecting anomalous data or outliers in these data sets is crucial for discovering inefficiencies, rare events, the root cause of issues, or opportunities for operational improvements. An anomaly refers to data points that fall outside of an operation’s normal behavior. Detecting anomalies is important because it helps businesses understand and protect themselves, as anomalies can indicate cybersecurity threats, successful marketing initiatives, or other important events. IT departments working in data science face the challenge of making sense of expanding and ever-changing data points. Machine learning techniques, powered by artificial intelligence (AI), offer three different methods for detecting anomalous behavior: supervised anomaly detection, unsupervised anomaly detection, and semi-supervised anomaly detection.
Supervised anomaly detection involves using real-world input and output data to detect anomalies. Data points are labeled as either normal or abnormal by a data analyst and used as training data for a machine learning model. This type of machine learning is useful for known outlier detection but cannot discover unknown anomalies or predict future issues. Common machine learning algorithms for supervised learning include the K-nearest neighbor (KNN) algorithm and the Local outlier factor (LOF) algorithm.
Unsupervised anomaly detection techniques do not require labeled data and can handle more complex data sets. These techniques use deep learning, neural networks, or auto encoders to find patterns in input data and identify anomalies. However, results gathered through unsupervised learning should be monitored as these techniques can incorrectly label anomalies. Machine learning algorithms for unstructured data include the K-means algorithm, the Isolation forest algorithm, and the One-class support vector machine (SVM) algorithm.
Semi-supervised anomaly detection methods combine the benefits of supervised and unsupervised learning. They use unsupervised learning to automate feature learning and work with unstructured data, but also incorporate human supervision to monitor and control the patterns the model learns. Linear regression is a common semi-supervised anomaly detection technique.
Anomaly detection is important across various industries. The choice of supervised, unsupervised, or semi-supervised learning algorithms depends on the type of data and the operational challenge being solved. Examples of use cases for anomaly detection include retail sales prediction, weather forecasting, intrusion detection systems, manufacturing predictive maintenance, medical image analysis, and fraud detection.
Observability in anomaly detection is enhanced by solutions and tools that provide greater visibility into performance data. IBM Instana Observability and IBM watsonx.ai are two powerful tools that leverage AI and machine learning to accurately predict, troubleshoot, and extract insights from large data sets.
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