How Does Machine Learning Find Anomalies in Unsupervised Data?

In unsupervised machine learning, anomaly detection is the process of identifying patterns or data points that deviate significantly from the normal behavior of the dataset. Several approaches can be used to find anomalies in unsupervised data, including the following:

Statistical Methods

Statistical techniques assume that anomalies are rare occurrences that differ significantly from the rest of the data in terms of statistical properties. Common statistical approaches include using measures such as mean, standard deviation, or percentile ranks to identify data points that fall outside a specified range or have extreme values.


Clustering algorithms group similar data points together based on their characteristics. Anomalies can be identified as data points that do not belong to any cluster or form their own cluster with significantly fewer members. Outlier detection algorithms like DBSCAN (Density-Based Spatial Clustering of Applications with Noise) and LOF (Local Outlier Factor) are commonly used for this purpose.

Dimensionality Reduction

Dimensionality reduction techniques, such as Principal Component Analysis (PCA), can be used to reduce the dimensionality of the dataset while retaining its essential characteristics. Anomalies can be identified by measuring the reconstruction error or the deviation of data points from the reduced-dimensional representation. 

Density Estimation

Density-based anomaly detection methods estimate the probability density function of the dataset. Data points that have low probability density or fall in regions with low density are considered anomalies. Kernel Density Estimation (KDE) and Gaussian Mixture Models (GMM) are commonly used density estimation techniques.


Autoencoders are neural networks trained to reconstruct their input data. Anomalies can be detected by measuring the reconstruction error, where data points with higher reconstruction errors are considered anomalous. Autoencoders can learn to reconstruct normal patterns and struggle to reconstruct anomalies.

One-Class SVM

Support Vector Machines (SVMs) can be trained in a one-class setting where the algorithm learns the boundaries of the normal data. Data points falling outside these boundaries are considered anomalies. This method is useful when the normal data is well-represented but anomalies are scarce.

It’s important to note that different datasets and anomaly detection tasks may require specific techniques or a combination of multiple approaches. The choice of method depends on the characteristics of the data, the availability of labeled anomalies (if any), and the specific requirements of the application.

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