Klasterisasi dan reduksi dimensi

61 metode dalam keluarga ini.

Unggulan

Aturan Asosiasi Pembelajaran AktifActive 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 Deteksi Anomali Autoencoder Pembelajaran AktifActive Learning Autoencoder Anomaly Detection combines an autoencoder's unsupervised reconstruction-error scoring with an active learning query loop. The model flags high-error ins Isolation Forest Pembelajaran AktifActive 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 Clustering Propagasi AfinitasAffinity propagation, introduced by Brendan Frey and Delbert Dueck in 2007, is a clustering algorithm that identifies representative 'exemplars' among the data by exchanging messag Algoritma AprioriThe Apriori algorithm, introduced by Agrawal and Srikant in 1994, is the foundational method for discovering frequent itemsets and association rules in transactional databases. It Penambangan Aturan Asosiasi (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

Jalur bacaan

Metode fondasional yang paling banyak dirujuk pada topik ini, dalam urutan pengembangannya — tempat untuk memulai jika Anda baru di sini.

Pengelompokan Hirarkis1963oleh Ward, J. H.
Clustering K-means1967 (formalized 1982)oleh MacQueen, J. B.; Lloyd, S. P.
Klasterisasi K-Means1967oleh MacQueen, J.
DBSCAN1996oleh Ester, M., Kriegel, H.-P., Sander, J. & Xu, X.
SVM Satu Kelas1999–2001oleh Scholkopf, B., Platt, J. C., Smola, A. J., Williamson, R. C.
Analisis Komponen Utama2002oleh Jolliffe, I.T. (textbook); Pearson & Hotelling (origins)
Deteksi Anomali Autoencoder2006–2014oleh Hinton, G. E. & Salakhutdinov, R. R. (autoencoders); applied to anomaly detection through multiple authors in the 2010s
Isolation Forest2008oleh Liu, F.T., Ting, K.M. & Zhou, Z.-H.

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Semua metode 61

Aturan Asosiasi Pembelajaran Aktif Deteksi Anomali Autoencoder Pembelajaran Aktif Isolation Forest Pembelajaran Aktif Clustering Propagasi Afinitas Algoritma Apriori Penambangan Aturan Asosiasi (Apriori)Aturan Asosiasi Deteksi Anomali Autoencoder BIRCH DBSCAN Penambangan Itemset Frekuen ECLAT Algoritma Ensemble Apriori Aturan Asosiasi Ensemble Deteksi Anomali Ensemble Autoencoder Ensemble HDBSCAN Ensemble Isolation Forest K-means Ensemble Clustering C-Means Kabur (FCM)Model Campuran Gaussian HDBSCAN Pengelompokan Hirarkis Isolation Forest Clustering K-means Klasterisasi K-Means PCA Kernel Local Outlier Factor (LOF)Embedding Linear Lokal (LLE)Mean Shift SVM Satu Kelas Aturan Asosiasi Daring Deteksi Anomali Autoencoder Daring DBSCAN Daring HDBSCAN Daring Isolation Forest Daring K-means Daring OPTICS Analisis Komponen Utama Regresi Komponen Utama (PCR)Proyeksi Acak Model Campuran Gaussian Teregulasi Pengelompokan K-Means Teregulasi Deteksi Anomali Autoencoder Robust HDBSCAN Robust Robust Isolation Forest Robust k-means Peta Pengorganisasi Mandiri (Peta Kohonen)Deteksi Anomali Autoencoder yang Diawasi Mandiri DBSCAN yang diawasi mandiri Model Gaussian Mixture Mandiri-Terbimbing (SS-GMM)Isolation Forest yang disupervisi mandiri K-means yang diawasi mandiri Algoritma Apriori Semi-terawasi Semi-supervised Association Rules Deteksi Anomali Semi-terawasi dengan Autoencoder Semi-supervised DBSCAN HDBSCAN Semi-Terawasi Semi-supervised Isolation Forest K-means Semi-Terawasi Spectral Clustering t-SNE UMAP