anomaly detection the process of anomaly detection is also termed as outlier detectionwhere we are interested in finding out occurrences of rare events or observations that typically do not occur normally based on historical data samples sometimes anomalies occur infrequently and are thus rare eventsand in other instancesanomalies might not be rare but might occur in very short bursts over timethus have specific patterns unsupervised learning methods can be used for anomaly detection such that we train the algorithm on the training dataset having normalnon-anomalous data samples once it learns the necessary data representationspatternsand relations among attributes in normal samplesfor any new data sampleit would be able to identify it as anomalous or normal data point by using its learned knowledge figure - depicts some typical anomaly detection based scenarios where you could apply supervised methods like one-class svm and unsupervised methods like clusteringk-nearest neighborsauto-encodersand so on to detect anomalies based on data and its features figure - unsupervised learninganomaly detection anomaly detection based methods are extremely popular in real-world scenarios like detection of security attacks or breachescredit card fraudmanufacturing anomaliesnetwork issuesand many more association rule-mining typically association rule-mining is data mining method use to examine and analyze large transactional datasets to find patterns and rules of interest these patterns represent interesting relationships and associationsamong various items across transactions association rule-mining is also often termed as market basket analysiswhich is used to analyze customer shopping patterns association rules help in detecting and predicting transactional patterns based on the knowledge it gains from training transactions using this techniquewe can answer questions like what items do people tend to buy togetherthereby indicating frequent item sets we can also associate or correlate products and itemsi insights like people who buy beer also tend to buy chicken wings at pub figure - shows how typical association rulemining method should work ideally on transactional dataset