Related references
Note: Only part of the references are listed.Mint: MDL-based approach for Mining INTeresting Numerical Pattern Sets
Tatiana Makhalova et al.
DATA MINING AND KNOWLEDGE DISCOVERY (2022)
For real: a thorough look at numeric attributes in subgroup discovery
Marvin Meeng et al.
DATA MINING AND KNOWLEDGE DISCOVERY (2021)
Discovering Outstanding Subgroup Lists for Numeric Targets Using MDL
Hugo M. Proenca et al.
MACHINE LEARNING AND KNOWLEDGE DISCOVERY IN DATABASES, ECML PKDD 2020, PT I (2021)
Interpretable multiclass classification by MDL-based rule lists
Hugo M. Proenca et al.
INFORMATION SCIENCES (2020)
Uni- and multivariate probability density models for numeric subgroup discovery
Marvin Meeng et al.
INTELLIGENT DATA ANALYSIS (2020)
Sets of Robust Rules, and How to Find Them
Jonas Fischer et al.
MACHINE LEARNING AND KNOWLEDGE DISCOVERY IN DATABASES, ECML PKDD 2019, PT I (2020)
Dataset of academic performance evolution for engineering students
Enrique Delahoz-Dominguez et al.
DATA IN BRIEF (2020)
A tutorial on statistically sound pattern discovery
Wilhelmiina Hamalainen et al.
DATA MINING AND KNOWLEDGE DISCOVERY (2019)
FSSD - A Fast and Efficient Algorithm for Subgroup Set Discovery
Adnene Belfodil et al.
2019 IEEE INTERNATIONAL CONFERENCE ON DATA SCIENCE AND ADVANCED ANALYTICS (DSAA 2019) (2019)
Minimum description length revisited
Peter Grunwald et al.
INTERNATIONAL JOURNAL OF MATHEMATICS FOR INDUSTRY (2019)
Effects of Pacing Properties on Performance in Long-Distance Running
Arie-Willem De Leeuw et al.
BIG DATA (2018)
Anytime discovery of a diverse set of patterns with Monte Carlo tree search
Guillaume Bosc et al.
DATA MINING AND KNOWLEDGE DISCOVERY (2018)
Subjectively Interesting Subgroup Discovery on Real-valued Targets
Jefrey Lijffijt et al.
2018 IEEE 34TH INTERNATIONAL CONFERENCE ON DATA ENGINEERING (ICDE) (2018)
Uncovering structure-property relationships of materials by subgroup discovery
Bryan R. Goldsmith et al.
NEW JOURNAL OF PHYSICS (2017)
Identifying consistent statements about numerical data with dispersion-corrected subgroup discovery
Mario Boley et al.
DATA MINING AND KNOWLEDGE DISCOVERY (2017)
Learning Certifiably Optimal Rule Lists
Elaine Angelino et al.
KDD'17: PROCEEDINGS OF THE 23RD ACM SIGKDD INTERNATIONAL CONFERENCE ON KNOWLEDGE DISCOVERY AND DATA MINING (2017)
Expect the Unexpected - On the Significance of Subgroups
Matthijs van Leeuwen et al.
DISCOVERY SCIENCE, (DS 2016) (2016)
INTERPRETABLE CLASSIFIERS USING RULES AND BAYESIAN ANALYSIS: BUILDING A BETTER STROKE PREDICTION MODEL
Benjamin Letham et al.
ANNALS OF APPLIED STATISTICS (2015)
Association Discovery in Two-View Data
Matthijs van Leeuwen et al.
IEEE TRANSACTIONS ON KNOWLEDGE AND DATA ENGINEERING (2015)
Subgroup discovery
Martin Atzmueller
WILEY INTERDISCIPLINARY REVIEWS-DATA MINING AND KNOWLEDGE DISCOVERY (2015)
The Difference and the Norm - Characterising Similarities and Differences Between Databases
Kailash Budhathoki et al.
MACHINE LEARNING AND KNOWLEDGE DISCOVERY IN DATABASES, ECML PKDD 2015, PT II (2015)
Subgroup Discovery in Smart Electricity Meter Data
Nanlin Jin et al.
IEEE TRANSACTIONS ON INDUSTRIAL INFORMATICS (2014)
Overview on evolutionary subgroup discovery: analysis of the suitability and potential of the search performed by evolutionary algorithms
Cristobal J. Carmona et al.
WILEY INTERDISCIPLINARY REVIEWS-DATA MINING AND KNOWLEDGE DISCOVERY (2014)
Diverse subgroup set discovery
Matthijs van Leeuwen et al.
DATA MINING AND KNOWLEDGE DISCOVERY (2012)
Kingfisher: an efficient algorithm for searching for both positive and negative dependency rules with statistical significance measures
Wilhelmiina Hamalainen
KNOWLEDGE AND INFORMATION SYSTEMS (2012)
Krimp: mining itemsets that compress
Jilles Vreeken et al.
DATA MINING AND KNOWLEDGE DISCOVERY (2011)
An overview on subgroup discovery: foundations and applications
Franciso Herrera et al.
KNOWLEDGE AND INFORMATION SYSTEMS (2011)
Maximal exceptions with minimal descriptions
Matthijs van Leeuwen
DATA MINING AND KNOWLEDGE DISCOVERY (2010)
NMEEF-SD: Non-dominated Multiobjective Evolutionary Algorithm for Extracting Fuzzy Rules in Subgroup Discovery
Cristobal Jose Carmona et al.
IEEE TRANSACTIONS ON FUZZY SYSTEMS (2010)
On subgroup discovery in numerical domains
Henrik Grosskreutz et al.
DATA MINING AND KNOWLEDGE DISCOVERY (2009)
Bayesian t tests for accepting and rejecting the null hypothesis
Jeffrey N. Rouder et al.
PSYCHONOMIC BULLETIN & REVIEW (2009)
Discovering significant patterns
Geoffrey I. Webb
MACHINE LEARNING (2007)
APRIORI-SD: Adapting association rule learning to subgroup discovery
Branko Kavsek et al.
APPLIED ARTIFICIAL INTELLIGENCE (2006)
The Bayesian two-sample t test
M Gönen et al.
AMERICAN STATISTICIAN (2005)