Why Anomaly Detection?
Machine Learning has four common classes of applications: classification, predicting next value, anomaly detection, and discovering structure. Among them, Anomaly detection detects data points in data that does not fit well with the rest of the data. It has a wide range of applications such as fraud detection, surveillance, diagnosis, data cleanup, and predictive maintenance.
Although it has been studied in detail in academia, applications of anomaly detection have been limited to niche domains like banks, financial institutions, auditing, and medical diagnosis etc. However, with the advent of IoT, anomaly detection would likely to play a key role in IoT use cases such as monitoring and predictive maintenance.
This post explores what is anomaly detection, different anomaly detection techniques, discusses the key idea behind those techniques, and wraps up with a discussion on how to make use of those results.
Is it not just Classification?
The answer is yes if…
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