Data Mining and Artificial Intelligence

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

• Blixem: A LiXQuery engine (by Jeroen Avonts, Pieter Wellens, Wim Le Page)
• Conqueror: Conjunctive Query Generator (by Wim Le Page)

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)