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.

Asynchronous dual-pipeline deep learning framework for online data stream mining

This code belongs to ACL conference paper entitled as "An Online Semantic-enhanced Dirichlet Model for Short Text Stream Clustering"

Realtime Data Processing and Search Engine Implementation.

Repository for the StreamingRandomPatches algorithm implemented in MOA 2019.04

ClipStream - multiple data streams clustering method

Opinion Stream Mining

This is a anomaly detection is real-time using K-means

Hierarchical Distributed Stream Miner: A Distributed Data Mining Approach to Classifying Heterogeneous Data Streams

A repro with reports of the assignments of the master degree in data science

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.