Algorithms for clustering data
Algorithms for clustering data
Mining association rules between sets of items in large databases
SIGMOD '93 Proceedings of the 1993 ACM SIGMOD international conference on Management of data
Computing iceberg concept lattices with TITANIC
Data & Knowledge Engineering
Free-Sets: A Condensed Representation of Boolean Data for the Approximation of Frequency Queries
Data Mining and Knowledge Discovery
Knowledge Acquisition Via Incremental Conceptual Clustering
Machine Learning
CMAR: Accurate and Efficient Classification Based on Multiple Class-Association Rules
ICDM '01 Proceedings of the 2001 IEEE International Conference on Data Mining
Biclustering of Expression Data
Proceedings of the Eighth International Conference on Intelligent Systems for Molecular Biology
Approximation of Frequency Queris by Means of Free-Sets
PKDD '00 Proceedings of the 4th European Conference on Principles of Data Mining and Knowledge Discovery
Efficient Local Search in Conceptual Clustering
DS '01 Proceedings of the 4th International Conference on Discovery Science
Information-theoretic co-clustering
Proceedings of the ninth ACM SIGKDD international conference on Knowledge discovery and data mining
Biclustering Algorithms for Biological Data Analysis: A Survey
IEEE/ACM Transactions on Computational Biology and Bioinformatics (TCBB)
Expert-guided subgroup discovery: methodology and application
Journal of Artificial Intelligence Research
Mining formal concepts with a bounded number of exceptions from transactional data
KDID'04 Proceedings of the Third international conference on Knowledge Discovery in Inductive Databases
Supporting bi-cluster interpretation in 0/1 data by means of local patterns
Intelligent Data Analysis - Selected papers from IDA2005, Madrid, Spain
Actionability and formal concepts: a data mining perspective
ICFCA'08 Proceedings of the 6th international conference on Formal concept analysis
Constraint-Based mining of fault-tolerant patterns from boolean data
KDID'05 Proceedings of the 4th international conference on Knowledge Discovery in Inductive Databases
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Clustering or bi-clustering techniques have been proved quite useful in many application domains. A weakness of these techniques remains the poor support for grouping characterization. We consider eventually large Boolean data sets which record properties of objects and we assume that a bi-partition is available. We introduce a generic cluster characterization technique which is based on collections of bi-sets (i.e., sets of objects associated to sets of properties) which satisfy some user-defined constraints, and a measure of the accuracy of a given bi-set as a bi-cluster characterization pattern. The method is illustrated on both formal concepts (i.e., “maximal rectangles of true values”) and the new type of δ-bi-sets (i.e., “rectangles of true values with a bounded number of exceptions per column”). The added-value is illustrated on benchmark data and two real data sets which are intrinsically noisy: a medical data about meningitis and Plasmodium falciparum gene expression data.