When working with data, not every point follows the same pattern. Some values look strange, behave differently, or don’t match the overall trend. These are called outliers, and the process of finding them is known as outlier analysis in data mining.
Outliers are not always bad data. They could be signs of fraud, system errors, or even valuable hidden insights. For example, if a sensor usually reports 80°C but suddenly logs 150°C, that unusual reading might indicate an overheating issue. Ignoring such points can mean missing critical information.
Why Outlier Analysis Is Useful
Fraud detection: Detecting abnormal transactions in banking or e-commerce.
Data cleaning: Identifying mistakes like impossible age values.
Healthcare monitoring: Spotting sudden spikes in patient data.
Business optimisation: Understanding unusual customer behaviour.
Types of Outliers
Global Outliers – values that are very different from the rest.
Contextual Outliers – normal in one context but unusual in another.
Collective Outliers – groups of values that look abnormal together.
Methods to Detect Outliers
Statistical methods (mean, standard deviation, z-scores)
Distance-based (k-Nearest Neighbours)
Density-based (Local Outlier Factor)
Clustering-based (K-means, DBSCAN)
Machine learning models (Isolation Forest, Autoencoders, One-Class SVM)
Closing Note
Whether you’re into data science, analytics, or just exploring datasets, outlier analysis is a powerful skill. It improves data quality, enhances machine learning models, and often reveals insights hidden in plain sight.
If you’re starting out, check out beginner-friendly resources and courses like those at Ze Learning Labb, where these techniques are taught with real-world projects.
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