Classification & Anomaly detection
- LCIF: Combining Instance and Feature neighbors for Efficient Multi-label Classification (by Len Feremans)
- PBAD: Pattern-Based Anomaly Detection in Mixed-Type Time Series (by Len Feremans)
- TIPM: Interactive time series pattern mining and anomaly detection in multi-dimensional time series and event logs (by Len Feremans)
- EDBN: 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
- CFD Discovery Algorithms: Implementations for discovering frequent, approximate Conditional Functional Dependencies from csv data (by Joeri Rammelaere)
- XPlode: 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)
- FBIMiner: 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)
- CTane and CFDMiner: Implementations of the CTane and CFDMiner algorithms for discovering Conditional Functional Dependencies.
Databases & Query languages
Frequent Pattern Mining
- Apriori, NDI, Eclat, FP-growth, DIC, Rules (by Bart Goethals)
- BigFIM (by Sandy Moens, Emin Aksehirli)
- SMuRFIG: Simple Multi-Relational Frequent Itemset Generator (by Michael Mampaey, Wim Le Page)
Interactive & Efficient Pattern Mining
- MIME & SNIPER, Direct Pattern Sampling using CFTP, Random Maximal Itemset Sampling, Recursive Tile Sampling (by Sandy Moens)
- TIPM: Interactive time series pattern mining and anomaly detection in multi-dimensional time series and event logs (by Len Feremans)
- μ-Miner, XMiner, Supporter, Eclat, RDB generator, FeaST (by Michael Mampaey)
Pattern mining on sequential data & Interestingness measures
- FCI seq: Efficient Discovery of Sets of Co-occurring Items in Event Sequences (by Len Feremans)
- FCI extended: Efficiently Mining Cohesion-based Patterns and Rules in Event Sequences (by Len Feremans)
- Mining Closed Strict Episodes and Marbles (by Nikolaj Tatti)
- SCII: Sequence Classification based on Interesting Itemsets (by Cheng Zhou)
- SQS: The Long and the Short of It: Summarizing Event Sequences with Serial Episodes (by Nikolaj Tatti, Jilles Vreeken)
- QCSP: Mining Top-k Quantile-based Cohesive Sequential Patterns (by Len Feremans)
Pattern sets & Summarisation
- Comparing Apples and Oranges (by Nikolaj Tatti)
- Finding Robust Itemsets Under Subsampling (by Nikolaj Tatti)
- MTV: Succinctly Summarizing Data with Itemsets (by Michael Mampaey)
- Slim: Directly Mining Descriptive Patterns (by Koen Smets, Jilles Vreeken)
- STIJL: Discovering Descriptive Tile Trees by Mining Optimal Geometric Subtiles (by Nikolaj Tatti, Jilles Vreeken)
- Summarising Data by Clustering Attributes (by Michael Mampaey)
- Tiling Databases (by Koen Smets, Jilles Vreeken)
- Using background knowledge to rank itemsets (by Nikolaj Tatti)