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.
In this commit, a new tab panel for Semi-supervised Learning will be pushed to MOA. New functionalities include:
Semi-supervised streams that can simulate a stream with randomly removed labels
New learners for SSL found in classifiers/semisupervised (Cluster-n-Label & Self-training)
CluStream also has an adaptation to make it compatible to use with SSL learners (clusterers/semisupervised)
Attribute Observers that compute the similarity between categorical attributes according to [1], useful for computing distance for clustering module
[1] S. Boriah, V. Chandola, V. Kumar. "Similarity Measures for Categorical Data: A Comparative Evaluation."