Mining association rules between sets of items in large databases
SIGMOD '93 Proceedings of the 1993 ACM SIGMOD international conference on Management of data
Fast discovery of association rules
Advances in knowledge discovery and data mining
Exploratory mining and pruning optimizations of constrained associations rules
SIGMOD '98 Proceedings of the 1998 ACM SIGMOD international conference on Management of data
Efficient mining of association rules using closed itemset lattices
Information Systems
A fast algorithm for building lattices
Information Processing Letters
Mining frequent patterns with counting inference
ACM SIGKDD Explorations Newsletter - Special issue on “Scalable data mining algorithms”
Free-Sets: A Condensed Representation of Boolean Data for the Approximation of Frequency Queries
Data Mining and Knowledge Discovery
Using transposition for pattern discovery from microarray data
DMKD '03 Proceedings of the 8th ACM SIGMOD workshop on Research issues in data mining and knowledge discovery
Optimization of association rule mining queries
Intelligent Data Analysis
Iterative normalization of cDNA microarray data
IEEE Transactions on Information Technology in Biomedicine
Extending the state-of-the-art of constraint-based pattern discovery
Data & Knowledge Engineering
Soft constraint based pattern mining
Data & Knowledge Engineering
A model for managing collections of patterns
Proceedings of the 2007 ACM symposium on Applied computing
Description and simulation of dynamic mobility networks
Computer Networks: The International Journal of Computer and Telecommunications Networking
A constraint-based querying system for exploratory pattern discovery
Information Systems
Closed patterns meet n-ary relations
ACM Transactions on Knowledge Discovery from Data (TKDD)
A framework for mining top-k frequent closed itemsets using order preserving generators
Proceedings of the 2nd Bangalore Annual Compute Conference
Finding Top-N Pseudo Formal Concepts with Core Intents
MLDM '09 Proceedings of the 6th International Conference on Machine Learning and Data Mining in Pattern Recognition
Mining bi-sets in numerical data
KDID'06 Proceedings of the 5th international conference on Knowledge discovery in inductive databases
Extending the soft constraint based mining paradigm
KDID'06 Proceedings of the 5th international conference on Knowledge discovery in inductive databases
Actionability and formal concepts: a data mining perspective
ICFCA'08 Proceedings of the 6th international conference on Formal concept analysis
Towards constrained co-clustering in ordered 0/1 data sets
ISMIS'06 Proceedings of the 16th international conference on Foundations of Intelligent Systems
A bi-clustering framework for categorical data
PKDD'05 Proceedings of the 9th European conference on Principles and Practice of Knowledge Discovery in Databases
An algorithm for extracting rare concepts with concise intents
ICFCA'10 Proceedings of the 8th international conference on Formal Concept Analysis
LPD'04 Proceedings of the 2004 international conference on Local Pattern Detection
Local pattern discovery in Array-CGH data
LPD'04 Proceedings of the 2004 international conference on Local Pattern Detection
A survey on condensed representations for frequent sets
Proceedings of the 2004 European conference on Constraint-Based Mining and Inductive Databases
Contribution to gene expression data analysis by means of set pattern mining
Proceedings of the 2004 European conference on Constraint-Based Mining and Inductive Databases
Mining a new fault-tolerant pattern type as an alternative to formal concept discovery
ICCS'06 Proceedings of the 14th international conference on Conceptual Structures: inspiration and Application
Constraint-Based mining of fault-tolerant patterns from boolean data
KDID'05 Proceedings of the 4th international conference on Knowledge Discovery in Inductive Databases
Scalable mining of frequent tri-concepts from folksonomies
PAKDD'12 Proceedings of the 16th Pacific-Asia conference on Advances in Knowledge Discovery and Data Mining - Volume Part II
Closed and noise-tolerant patterns in n-ary relations
Data Mining and Knowledge Discovery
DisClose: discovering colossal closed itemsets via a memory efficient compact row-tree
PAKDD'12 Proceedings of the 2012 Pacific-Asia conference on Emerging Trends in Knowledge Discovery and Data Mining
Review: Formal Concept Analysis in knowledge processing: A survey on models and techniques
Expert Systems with Applications: An International Journal
Review: Formal concept analysis in knowledge processing: A survey on applications
Expert Systems with Applications: An International Journal
Key roles of closed sets and minimal generators in concise representations of frequent patterns
Intelligent Data Analysis
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We are designing new data mining techniques on boolean contexts to identify a priori interesting bi-sets, i.e., sets of objects (or transactions) and associated sets of attributes (or items). It improves the state of the art in many application domains where transactional/boolean data are to be mined (e.g., basket analysis, WWW usage mining, gene expression data analysis). The so-called (formal) concepts are important special cases of a priori interesting bi-sets that associate closed sets on both dimensions thanks to the Galois operators. Concept mining in boolean data is tractable provided that at least one of the dimensions (number of objects or attributes) is small enough and the data is not too dense. The task is extremely hard otherwise. Furthermore, it is important to enable user-defined constraints on the desired bi-sets and use them during the extraction to increase both the efficiency and the a priori interestingness of the extracted patterns. It leads us to the design of a new algorithm, called D-Miner, for mining concepts under constraints. We provide an experimental validation on benchmark data sets. Moreover, we introduce an original data mining technique for microarray data analysis. Not only boolean expression properties of genes are recorded but also we add biological information about transcription factors. In such a context, D-Miner can be used for concept mining under constraints and outperforms the other studied algorithms. We show also that data enrichment is useful for evaluating the biological relevancy of the extracted concepts.