Clustering & DR
61 methods in this family.
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Active learning Association rulesActive learning association rules combines the iterative query-and-label loop of active learning with association rule mining, allowing a human expert to guide the discovery procesActive Learning Autoencoder Anomaly DetectionActive Learning Autoencoder Anomaly Detection combines an autoencoder's unsupervised reconstruction-error scoring with an active learning query loop. The model flags high-error insActive learning Isolation forestActive Learning Isolation Forest combines the unsupervised anomaly-scoring power of Isolation Forest with an iterative query strategy that asks a human expert to label the most infAffinity PropagationAffinity propagation, introduced by Brendan Frey and Delbert Dueck in 2007, is a clustering algorithm that identifies representative 'exemplars' among the data by exchanging messagApriori AlgorithmThe Apriori algorithm, introduced by Agrawal and Srikant in 1994, is the foundational method for discovering frequent itemsets and association rules in transactional databases. It Association Rule MiningAssociation Rule Mining is an unsupervised data-mining technique that discovers co-occurrence patterns among items in transactional datasets. Formally introduced by Agrawal, Imieli
All methods 61
Active learning Association rulesActive Learning Autoencoder Anomaly DetectionActive learning Isolation forestAffinity PropagationApriori AlgorithmAssociation Rule MiningAssociation RulesAutoencoder Anomaly DetectionBIRCHDBSCANECLATEnsemble Apriori AlgorithmEnsemble Association RulesEnsemble Autoencoder Anomaly DetectionEnsemble HDBSCANEnsemble Isolation ForestEnsemble K-meansFuzzy C-MeansGaussian Mixture ModelHDBSCANHierarchical ClusteringIsolation ForestK-meansK-Means ClusteringKernel PCALocal Outlier FactorLocally Linear EmbeddingMean ShiftOne-class SVMOnline Association RulesOnline Autoencoder Anomaly DetectionOnline DBSCANOnline HDBSCANOnline Isolation ForestOnline K-meansOPTICSPrincipal Component AnalysisPrincipal Components RegressionRandom ProjectionRegularized Gaussian Mixture ModelRegularized k-meansRobust Autoencoder anomaly detectionRobust HDBSCANRobust Isolation forestRobust k-meansSelf-Organizing MapSelf-supervised Autoencoder Anomaly DetectionSelf-supervised DBSCANSelf-supervised Gaussian Mixture ModelSelf-supervised Isolation ForestSelf-supervised K-meansSemi-supervised Apriori AlgorithmSemi-supervised Association RulesSemi-supervised Autoencoder Anomaly DetectionSemi-supervised DBSCANSemi-supervised HDBSCANSemi-supervised Isolation ForestSemi-supervised K-meansSpectral Clusteringt-SNEUMAP