Clustering & dimensiereductie

61 methoden in deze familie.

Uitgelicht

Actieve Leren AssociatieregelsActive 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 proces Active 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 ins Actief Lerend IsolatiebosActive 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 inf Affinity Propagation clusteranalyseAffinity propagation, introduced by Brendan Frey and Delbert Dueck in 2007, is a clustering algorithm that identifies representative 'exemplars' among the data by exchanging messag Apriori-algoritmeThe Apriori algorithm, introduced by Agrawal and Srikant in 1994, is the foundational method for discovering frequent itemsets and association rules in transactional databases. It Associatieregels Leren (Apriori)Association Rule Mining is an unsupervised data-mining technique that discovers co-occurrence patterns among items in transactional datasets. Formally introduced by Agrawal, Imieli

Leesroute

De meest geraadpleegde fundamentele methoden van dit onderwerp, in de volgorde waarin ze zijn ontwikkeld — een plek om te beginnen als u hier nieuw bent.

Hiërarchische clustering1963door Ward, J. H.
K-means Clustering1967 (formalized 1982)door MacQueen, J. B.; Lloyd, S. P.
K-Means Clustering1967door MacQueen, J.
DBSCAN1996door Ester, M., Kriegel, H.-P., Sander, J. & Xu, X.
One-Class SVM1999–2001door Scholkopf, B., Platt, J. C., Smola, A. J., Williamson, R. C.
Hoofdcomponentenanalyse2002door Jolliffe, I.T. (textbook); Pearson & Hotelling (origins)
Autoencoder Anomaliedetectie2006–2014door Hinton, G. E. & Salakhutdinov, R. R. (autoencoders); applied to anomaly detection through multiple authors in the 2010s
Isolation Forest2008door Liu, F.T., Ting, K.M. & Zhou, Z.-H.

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Alle methoden 61

Actieve Leren Associatieregels Active Learning Autoencoder Anomaly Detection Actief Lerend Isolatiebos Affinity Propagation clusteranalyse Apriori-algoritme Associatieregels Leren (Apriori)Associatieregels Autoencoder Anomaliedetectie BIRCH DBSCAN ECLAT Frequent-Itemset Mining Ensemble Apriori Algoritme Ensemble Association Rules Ensemble Autoencoder Anomaliedetectie Ensemble HDBSCAN Ensemble Isolation Forest Ensemble K-means Fuzzy C-Means Clustering (FCM)Gaussiaans Mixture Model HDBSCAN Hiërarchische clustering Isolation Forest K-means Clustering K-Means Clustering Kernel PCA Local Outlier Factor (LOF)Lokaal Lineaire Inbedding (LLE)Mean Shift One-Class SVM Online Association Rules Online Autoencoder Anomaly Detection Online DBSCAN Online HDBSCAN Online Isolation Forest Online K-means OPTICS Hoofdcomponentenanalyse Principal Components Regressie Willekeurige projectie Geregulariseerd Gaussisch Mixture Model Geregulariseerde K-Means Clustering Robuuste Autoencoder Anomaliedetectie Robuuste HDBSCAN Robuuste Isolation Forest Robuuste k-gemiddelden Self-Organizing Map (Kohonen Map)Zelf-gesuperviseerde autoencoder anomaliedetectie Zelf-gesuperviseerde DBSCAN Zelf-gesuperviseerd Gaussisch Mengselmodel Zelf-gesuperviseerd Isolation Forest Zelf-gesuperviseerde K-means Semi-supervised Apriori Algoritme Semi-supervised Association Rules Semi-supervised Autoencoder Anomaly Detection Semi-supervised DBSCAN Semi-supervised HDBSCAN Semi-supervised Isolation Forest Semi-supervised K-means Spectrale Clustering t-SNE UMAP