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Research
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UAntwerp
Research groups
Adrem Data Lab
Research
Software
Data Mining and Artificial Intelligence
Research
Mission
Topics
Publications
Software
Data Mining and Artificial Intelligence
Bio-Informatics
Projects
Conferences
Software
Classification
LCIF
: Combining Instance and Feature neighbors for Efficient Multi-label Classification (by Len Feremans)
Databases / Query languages
Blixem
: A LiXQuery engine (by Jeroen Avonts, Pieter Wellens, Wim Le Page)
Conqueror
: Conjunctive Query Generator (by Wim Le Page)
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.
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 Pattern Mining / Efficient Pattern Mining
MIME
&
SNIPER
,
Direct Pattern Sampling using CFTP
,
Random Maximal Itemset Sampling
,
Recursive Tile Sampling
(by Sandy Moens)
μ-Miner, XMiner, Supporter, Eclat, RDB generator, FeaST
(by Michael Mampaey)
Pattern Sets / Summarization
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)
Pattern Mining / Sequential Data /Interestingness measures
FCI seq
: Efficient Discovery of Sets of Co-occurring Items 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)
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