In data science, also referred to as outlier detection and sometimes as novelty detection, is generally understood to be the identification of rare items, events or observations which deviate significantly from the majority of the data and do not conform to a well-defined notion of normal behavior. Anomaly detection is an important area of data science that can help identify outliers in datasets and uncover unexpected patterns. Anomalies are data points or events that do not conform to normal behaviour and therefore require further investigation. Anomaly detection techniques can be used for a wide variety of applications, such as fraud detection, medical diagnosis, fault detection, customer segmentation and more.

How it works ?

The first step in anomaly detection is to understand the nature of the dataset. This includes understanding the underlying distributions of each variable as well as any correlations or trends in the data. Once these patterns have been identified, various algorithms can then be applied to detect anomalous points or events.

Approaches for Anomaly Detection

Common approaches for anomaly detection include statistical methods where mathematical models are used to detect outliers; machine learning methods which use supervised or unsupervised learning algorithms; and artificial intelligence methods where expert systems and deep learning algorithms are employed to detect anomalies. Statistical approaches such as density-based clustering and principal component analysis (PCA) are commonly used for detecting outliers in datasets. Machine learning approaches include support vector machines (SVM), random forests, k-nearest neighbours (KNN), neural networks, decision trees, and others. Artificial intelligence methods include genetic algorithms, fuzzy logic systems, Bayesian networks, reinforcement learning systems, game theory models and more recent developments like deep learning architectures such as auto-encoders that have shown promising results in identifying anomalies from high dimensional datasets.

When using statistical or machine learning approaches for anomaly detection, it is important to select appropriate feature sets that allow for distinguishing between normal behaviour and abnormal behaviour. Feature engineering involves selecting relevant variables for the model so that it can accurately identify anomalies from unstructured data sources. Additionally, different tools may also be employed to automate feature selection processes such as Principal Component Analysis (PCA). 

Once features have been selected for modelling purposes, various algorithms must then be applied in order to choose one most suitable for the task at hand. There are numerous considerations when selecting an algorithm including the amount of computation needed; how many parameters need adjusting; how quickly the model should run; whether a supervised or unsupervised approach will work best; if online or offline processing will be necessary; if instance-based incremental updates are required; etc. Additionally, it is important to validate the model against different use cases before deploying it into production so that issues can be identified prior to deployment into live environments.