Classification & Anomaly detection
- : Combining Instance and Feature neighbors for Efficient Multi-label Classification (by Len Feremans)
- : Pattern-Based Anomaly Detection in Mixed-Type Time Series (by Len Feremans)
- : Interactive time series pattern mining and anomaly detection in multi-dimensional time series and event logs (by Len Feremans)
- : Extended Dynamic Bayesian Networks (by Stephen Pauwels)
- ACD2: A tool for detecting anomalies and concept drifts in business process logs (by Stephen Pauwels)
Data Quality Rules
- : Implementations for discovering frequent, approximate Conditional Functional Dependencies from csv data (by Joeri Rammelaere)
- : The XPlode algorithm discovers a Conditional Functional Dependency based on a given partial repair of a dataset. The returned CFD provides the best explanation for the observed repair (by Joeri Rammelaere)
- : Forbidden Itemsets are itemsets with a low lift, aiming to capture anomalous co-occurences in data, which in practice are often erroneous. The program further attempts to repair the data, in order to remove all forbidden itemsets (by Joeri Rammelaere)
- and : Implementations of the CTane and CFDMiner algorithms for discovering Conditional Functional Dependencies.
Databases & Query languages
- : A LiXQuery engine (by Jeroen Avonts, Pieter Wellens, Wim Le Page)
- : Conjunctive Query Generator (by Wim Le Page)
Frequent Pattern Mining
- (by Bart Goethals)
- (by Sandy Moens, Emin Aksehirli)
- : Simple Multi-Relational Frequent Itemset Generator (by Michael Mampaey, Wim Le Page)
Interactive & Efficient Pattern Mining
- & , , , (by Sandy Moens)
- : Interactive time series pattern mining and anomaly detection in multi-dimensional time series and event logs (by Len Feremans)
- (by Michael Mampaey)
Pattern mining on sequential data & Interestingness measures
- : Efficient Discovery of Sets of Co-occurring Items in Event Sequences (by Len Feremans)
- : Efficiently Mining Cohesion-based Patterns and Rules in Event Sequences (by Len Feremans)
- (by Nikolaj Tatti)
- : Sequence Classification based on Interesting Itemsets (by Cheng Zhou)
- : The Long and the Short of It: Summarizing Event Sequences with Serial Episodes (by Nikolaj Tatti, Jilles Vreeken)
- : Mining Top-k Quantile-based Cohesive Sequential Patterns (by Len Feremans)
Pattern sets & Summarisation
- (by Nikolaj Tatti)
- (by Nikolaj Tatti)
- : Succinctly Summarizing Data with Itemsets (by Michael Mampaey)
- : Directly Mining Descriptive Patterns (by Koen Smets, Jilles Vreeken)
- : Discovering Descriptive Tile Trees by Mining Optimal Geometric Subtiles (by Nikolaj Tatti, Jilles Vreeken)
- (by Michael Mampaey)
- (by Koen Smets, Jilles Vreeken)
- (by Nikolaj Tatti)