Explora: a multipattern and multistrategy discovery assistant
Advances in knowledge discovery and data mining
Mining frequent patterns without candidate generation
SIGMOD '00 Proceedings of the 2000 ACM SIGMOD international conference on Management of data
Bump hunting in high-dimensional data
Statistics and Computing
Classification Rule Learning with APRIORI-C
EPIA '01 Proceedings of the10th Portuguese Conference on Artificial Intelligence on Progress in Artificial Intelligence, Knowledge Extraction, Multi-agent Systems, Logic Programming and Constraint Solving
An Algorithm for Multi-relational Discovery of Subgroups
PKDD '97 Proceedings of the First European Symposium on Principles of Data Mining and Knowledge Discovery
Fast Algorithms for Mining Association Rules in Large Databases
VLDB '94 Proceedings of the 20th International Conference on Very Large Data Bases
Finding the most interesting patterns in a database quickly by using sequential sampling
The Journal of Machine Learning Research
Subgroup Discovery with CN2-SD
The Journal of Machine Learning Research
Exploiting background knowledge for knowledge-intensive subgroup discovery
IJCAI'05 Proceedings of the 19th international joint conference on Artificial intelligence
Rapid knowledge capture using subgroup discovery with incremental refinement
Proceedings of the 4th international conference on Knowledge capture
Making CN2-SD subgroup discovery algorithm scalable to large size data sets using instance selection
Expert Systems with Applications: An International Journal
Tight Optimistic Estimates for Fast Subgroup Discovery
ECML PKDD '08 Proceedings of the 2008 European Conference on Machine Learning and Knowledge Discovery in Databases - Part I
Expert Systems with Applications: An International Journal
Causal Subgroup Analysis for Detecting Confounding
Applications of Declarative Programming and Knowledge Management
Using Declarative Specifications of Domain Knowledge for Descriptive Data Mining
Applications of Declarative Programming and Knowledge Management
On subgroup discovery in numerical domains
Data Mining and Knowledge Discovery
Cluster-grouping: from subgroup discovery to clustering
Machine Learning
Fast Subgroup Discovery for Continuous Target Concepts
ISMIS '09 Proceedings of the 18th International Symposium on Foundations of Intelligent Systems
IDA '09 Proceedings of the 8th International Symposium on Intelligent Data Analysis: Advances in Intelligent Data Analysis VIII
Non-redundant Subgroup Discovery Using a Closure System
ECML PKDD '09 Proceedings of the European Conference on Machine Learning and Knowledge Discovery in Databases: Part I
IEEE Transactions on Fuzzy Systems
Subgroup discovery for election analysis: a case study in descriptive data mining
DS'10 Proceedings of the 13th international conference on Discovery science
A multi-objective evolutionary approach for subgroup discovery
HAIS'11 Proceedings of the 6th international conference on Hybrid artificial intelligent systems - Volume Part II
Expert Systems with Applications: An International Journal
Explaining subgroups through ontologies
PRICAI'12 Proceedings of the 12th Pacific Rim international conference on Trends in Artificial Intelligence
Generic pattern trees for exhaustive exceptional model mining
ECML PKDD'12 Proceedings of the 2012 European conference on Machine Learning and Knowledge Discovery in Databases - Volume Part II
Face-to-face contacts at a conference: dynamics of communities and roles
MSM'11 Proceedings of the 2011 international conference on Modeling and Mining Ubiquitous Social Media
Describing locations using tags and images: explorative pattern mining in social media
MSM'11 Proceedings of the 2011 international conference on Modeling and Mining Ubiquitous Social Media
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In this paper we present the novel SD-Map algorithm for exhaustive but efficient subgroup discovery. SD-Map guarantees to identify all interesting subgroup patterns contained in a data set, in contrast to heuristic or sampling-based methods. The SD-Map algorithm utilizes the well-known FP-growth method for mining association rules with adaptations for the subgroup discovery task. We show how SD-Map can handle missing values, and provide an experimental evaluation of the performance of the algorithm using synthetic data.