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Anomaly detection related books, papers, videos, and toolboxes
Anomaly Detection Learning Resources is a curated open-source repository that collects educational materials, tools, and academic references related to anomaly detection and outlier analysis in data science. The project serves as a centralized index for researchers and practitioners who want to explore algorithms, datasets, and publications associated with detecting unusual patterns in data.
A framework for learning from a continuous supply of examples, a data stream. Includes classification, regression, clustering, outlierdetection and recommender systems. Related to the WEKA project, also written in Java, while scaling to adaptive large scale machine learning.
weka outlier is an implementation of outlierdetection algorithms for WEKA.
CODB (Class Outliers: Distance-Based) Algorithm is the first algorithm developed using WEKA framework.