**Magnum Opus** is commercial association discovery software that implements many of my association discovery techniques. It is now a core component in BigML.

**OPUS Miner** is an open source implementation of the OPUS Miner algorithm which applies OPUS search for Filtered Top-k Association Discovery of Self-Sufficient Itemsets. An R package is available here.

**Chordalysis** implements our approaches to scalable learning of graphical models.

We have contributed numerous components to the Weka machine learning workbench. These include:

**AnDE**: averaged n-dependence estimators, an efficient technique for relaxing the attribute-independence assumption of naive Bayes. [papers]**AODE**: averaged one-dependence estimators, AnDE with n=1. [papers]**AODEsr**: AODE with subsumption resolution. [papers]**BVDecomposeSegCVSub:**Bias-variance decomposition using the sub-sampled cross-validation procedure. [paper]**J48Graft**: adds grafting to J48. [ papers ]**LBR**: lazy Bayesian rules, a lazy learning approach to lessening the attribute-independence assumption of naive Bayes. [papers]**MultiBoostAB**: an ensemble learning technique that combines boosting and bagging, attaining much of the former’s superior bias reduction together with much of the latter’s superior variance reduction. [papers]**PKIDiscretize**: proportional k-interval discretization, a discretization technique for naive Bayes. [papers]**WANBIA**: a system that uses naive Bayes to precondition logistic regression [ papers ]

An **AnDE** package for R is available here. [papers]

**SKDB** is an open source C++ implementation of Selective KDB. A refinement that uses Hierarchical Dirichlet Processes to obtain exceptional predictive accuracy can be downloaded here.

**SASANDE** is an open source C++ implementation of Sample-based Selective Attribute ANDE.

**ALR** is an open source implementation of our big models for big data learning algorithm.

**EBNC** implements our algorithms for Efficient Parameter Learning of Bayesian Networks.

**Softmax Logistic Regression** (for both Continuous and Discrete data) – with TRON + QuasiNewton + Conjugate Gradient optimisations.

**The Knowledge Factory** is an expert system development environment that incorporates **interactive rule induction**. The Knowledge Factory works with you to produce and refine expert systems.

**C4.5X** is a set of source files that extends C4.5 release 6 to add decision tree grafting.

Our software for generating synthetic data streams with abrupt drift can be downloaded here.

Our system for describing the concept drift present in real-world data can be downloaded here.

Our packages for **time series classification** include **Barycentric averaging**, **fast indexing** (**Matlab version**), and **fast window size selection**.

One of my MDS students, Jieshen Huang, implemented impact rules in Python. The source can be found here.

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