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Cluster Analysis groups similar instances in data, which can be used in fraud detection to find groups of similar transactions or accounts. Fraudulent activities might cluster together due to common characteristics. For instance, K-means clustering might be used to group customers and reveal clusters with high chargeback rates.

Anomaly Detection involves identifying patterns in data that do not conform to expected behavior. In fraud detection, this can help to spot unusual transactions, such as large purchases from a new location, which could indicate credit card fraud. Examples include statistical methods, machine learning algorithms like Isolation Forest or neural networks designed to spot outliers.

Classification methods are used to assign labels to instances based on input features. In fraud detection, algorithms such as Decision Trees, Support Vector Machines, or Neural Networks are trained with features of transactions to classify them as fraudulent or legitimate. Examples include email spam filters that categorize emails as spam or not spam.

Text Mining is the process of deriving high-quality information from text. In fraud detection, text mining can be used to scan for deceptive language or other indicators of fraud in communication and documents. For example, using Natural Language Processing (NLP) techniques to analyze customer reviews or support tickets for signs of deceit.

Association Rule Learning uncovers relationships between variables in large databases. Fraud detection can use this technique to identify rules that indicate fraudulent behavior, like the combination of items frequently purchased together in credit card fraud. An example algorithm is Apriori, which might discover that transactions involving certain high-risk items often lead to chargebacks.