Algorithms for clustering data
Algorithms for clustering data
Knowledge Acquisition Via Incremental Conceptual Clustering
Machine Learning
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)
Constraint-based concept mining and its application to microarray data analysis
Intelligent Data Analysis
Out-of-core coherent closed quasi-clique mining from large dense graph databases
ACM Transactions on Database Systems (TODS)
Strategies for Identifying Statistically Significant Dense Regions in Microarray Data
IEEE/ACM Transactions on Computational Biology and Bioinformatics (TCBB)
Discovering Knowledge from Local Patterns with Global Constraints
ICCSA '08 Proceedings of the international conference on Computational Science and Its Applications, Part II
Data Mining and Knowledge Discovery
Actionability and formal concepts: a data mining perspective
ICFCA'08 Proceedings of the 6th international conference on Formal concept analysis
Summarising data by clustering items
ECML PKDD'10 Proceedings of the 2010 European conference on Machine learning and knowledge discovery in databases: Part II
Comparing apples and oranges: measuring differences between data mining results
ECML PKDD'11 Proceedings of the 2011 European conference on Machine learning and knowledge discovery in databases - Volume Part III
Visualizing transactional data with multiple clusterings for knowledge discovery
ISMIS'06 Proceedings of the 16th international conference on Foundations of Intelligent Systems
Towards constrained co-clustering in ordered 0/1 data sets
ISMIS'06 Proceedings of the 16th international conference on Foundations of Intelligent Systems
Constraint-Based mining of fault-tolerant patterns from boolean data
KDID'05 Proceedings of the 4th international conference on Knowledge Discovery in Inductive Databases
Discovering descriptive tile trees: by mining optimal geometric subtiles
ECML PKDD'12 Proceedings of the 2012 European conference on Machine Learning and Knowledge Discovery in Databases - Volume Part I
Summarizing categorical data by clustering attributes
Data Mining and Knowledge Discovery
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Bi-clustering is a promising conceptual clustering approach. Within categorical data, it provides a collection of (possibly overlapping) bi-clusters, i.e., linked clusters for both objects and attribute-value pairs. We propose a generic framework for bi-clustering which enables to compute a bi-partition from collections of local patterns which capture locally strong associations between objects and properties. To validate this framework, we have studied in details the instance CDK-Means. It is a K-Means-like clustering on collections of formal concepts, i.e., connected closed sets on both dimensions. It enables to build bi-partitions with a user control on overlapping between bi-clusters. We provide an experimental validation on many benchmark datasets and discuss the interestingness of the computed bi-partitions.