MOA is an open source framework for Big Data stream mining. It includes a collection of machine learning algorithms (classification, regression, clustering, outlier detection, concept drift detection and recommender systems) and tools for evaluation.
Fwumious Wabbit, fast on-line machine learning toolkit written in Rust
A Python library for efficient feature ranking and selection on sparse data sets.
This code belongs to ACL conference paper entitled as "An Online Semantic-enhanced Dirichlet Model for Short Text Stream Clustering"
Asynchronous dual-pipeline deep learning framework for online data stream mining
Realtime Data Processing and Search Engine Implementation.
Repository for the StreamingRandomPatches algorithm implemented in MOA 2019.04
ClipStream - multiple data streams clustering method
This is a anomaly detection is real-time using K-means
Opinion Stream Mining
SiameseDuo++ (Neurcomputing 2025)
Hoeffding Adaptive Tree Regressor for CapyMOA with MOA implementation, wrapper, and streaming regression benchmarks.
Hierarchical Distributed Stream Miner: A Distributed Data Mining Approach to Classifying Heterogeneous Data Streams
Transceiver Framework: A framework for concurrent multi stage processing of data & MDP: An online motif detector and predictor embedded in the transceiver framework.
Complex event processing for data stream preprocessing
Implementation of the PLStream locally on macOS.
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