Discrete Mathematics
Dynamic itemset counting and implication rules for market basket data
SIGMOD '97 Proceedings of the 1997 ACM SIGMOD international conference on Management of data
Fast discovery of association rules
Advances in knowledge discovery and data mining
Efficient mining of emerging patterns: discovering trends and differences
KDD '99 Proceedings of the fifth ACM SIGKDD international conference on Knowledge discovery and data mining
Generating non-redundant association rules
Proceedings of the sixth ACM SIGKDD international conference on Knowledge discovery and data mining
Robust Classification for Imprecise Environments
Machine Learning
Mining frequent patterns by pattern-growth: methodology and implications
ACM SIGKDD Explorations Newsletter - Special issue on “Scalable data mining algorithms”
Mining frequent patterns with counting inference
ACM SIGKDD Explorations Newsletter - Special issue on “Scalable data mining algorithms”
Formal Concept Analysis: Mathematical Foundations
Formal Concept Analysis: Mathematical Foundations
Feature Selection for Knowledge Discovery and Data Mining
Feature Selection for Knowledge Discovery and Data Mining
Detecting Group Differences: Mining Contrast Sets
Data Mining and Knowledge Discovery
Free-Sets: A Condensed Representation of Boolean Data for the Approximation of Frequency Queries
Data Mining and Knowledge Discovery
Scalable Algorithms for Association Mining
IEEE Transactions on Knowledge and Data Engineering
Machine Learning
Mining Bases for Association Rules Using Closed Sets
ICDE '00 Proceedings of the 16th International Conference on Data Engineering
Concept Data Analysis: Theory and Applications
Concept Data Analysis: Theory and Applications
Subgroup Discovery with CN2-SD
The Journal of Machine Learning Research
Advances in frequent itemset mining implementations: report on FIMI'03
ACM SIGKDD Explorations Newsletter - Special issue on learning from imbalanced datasets
Mining Non-Redundant Association Rules
Data Mining and Knowledge Discovery
Learning Rules from Highly Unbalanced Data Sets
ICDM '04 Proceedings of the Fourth IEEE International Conference on Data Mining
Essential classification rule sets
ACM Transactions on Database Systems (TODS)
Induction of comprehensible models for gene expression datasets by subgroup discovery methodology
Journal of Biomedical Informatics - Special issue: Biomedical machine learning
Introduction to Data Mining, (First Edition)
Introduction to Data Mining, (First Edition)
Data Mining: Practical Machine Learning Tools and Techniques, Second Edition (Morgan Kaufmann Series in Data Management Systems)
Expert-guided subgroup discovery: methodology and application
Journal of Artificial Intelligence Research
Condensed representation of EPs and patterns quantified by frequency-based measures
KDID'04 Proceedings of the Third international conference on Knowledge Discovery in Inductive Databases
PKDD'06 Proceedings of the 10th European conference on Principle and Practice of Knowledge Discovery in Databases
Relevancy in constraint-based subgroup discovery
Proceedings of the 2004 European conference on Constraint-Based Mining and Inductive Databases
Adaptive XML Tree Classification on Evolving Data Streams
ECML PKDD '09 Proceedings of the European Conference on Machine Learning and Knowledge Discovery in Databases: Part I
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
Adaptive Stream Mining: Pattern Learning and Mining from Evolving Data Streams
Proceedings of the 2010 conference on Adaptive Stream Mining: Pattern Learning and Mining from Evolving Data Streams
Transactions on rough sets XII
Fast and memory-efficient discovery of the top-k relevant subgroups in a reduced candidate space
ECML PKDD'11 Proceedings of the 2011 European conference on Machine learning and knowledge discovery in databases - Volume Part I
Non-redundant subgroup discovery in large and complex data
ECML PKDD'11 Proceedings of the 2011 European conference on Machine learning and knowledge discovery in databases - Volume Part III
An enhanced relevance criterion for more concise supervised pattern discovery
Proceedings of the 18th ACM SIGKDD international conference on Knowledge discovery and data mining
Good classification tests as formal concepts
ICFCA'12 Proceedings of the 10th international conference on Formal Concept Analysis
Closed and noise-tolerant patterns in n-ary relations
Data Mining and Knowledge Discovery
Key roles of closed sets and minimal generators in concise representations of frequent patterns
Intelligent Data Analysis
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Closed sets have been proven successful in the context of compacted data representation for association rule learning. However, their use is mainly descriptive, dealing only with unlabeled data. This paper shows that when considering labeled data, closed sets can be adapted for classification and discrimination purposes by conveniently contrasting covering properties on positive and negative examples. We formally prove that these sets characterize the space of relevant combinations of features for discriminating the target class. In practice, identifying relevant/irrelevant combinations of features through closed sets is useful in many applications: to compact emerging patterns of typical descriptive mining applications, to reduce the number of essential rules in classification, and to efficiently learn subgroup descriptions, as demonstrated in real-life subgroup discovery experiments on a high dimensional microarray data set.