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What is Anomaly Detection
Anomaly Detection is also known as the outlier detection. Anomaly detection is the process of identifying unusual events, items or observations in the dataset, which is suspicious because they are different from the standard data or pattern.
Many outlier detection algorithms, particularly unsupervised techniques, are not able to detect the unexpected jumps in the activity as an outlier. A cluster analysis algorithm, on the other hand, may typically detect these types of micro clusters more easily.
There are three types of techniques we can detect outliers : unsupervised. Semisupervised, and supervised. The correct anomaly detection method depends on the available labels in the data.
In supervised methods require labeled dataset for e.g labelled as “normal” and “anomoulas” involve training classifier. This method is rarely used in anomaly detection due to the general unavailability of labelled data and the inherent unbalanced nature of the classes. The most common supervised methods include Bayesian networks, k-nearest neighbors, decision trees, supervised neural networks, and SVMs. The advantage of supervised method is that they may higher rate of detection than unsupervised techniques.
In Semi-supervised anomaly detection method assume some portion of the data is labelled and some unlabeled. In this apply the classification algorithm on labelled data use that model to predict the status of the remaining data.
Unsupervised anomaly detection techniques assume that the data is unlabeled, and they are by far the most popular due to their broad and useful use. The most common unsupervised anomaly detection algorithms are k-meas, GMMs, hypothesis test based analysis, PCA and autoencoder.
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