Clustering by pattern similarity in large data sets
Proceedings of the 2002 ACM SIGMOD international conference on Management of data
OP-Cluster: Clustering by Tendency in High Dimensional Space
ICDM '03 Proceedings of the Third IEEE International Conference on Data Mining
Generalizing the notion of support
Proceedings of the tenth 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)
Quantitative Association Rules Based on Half-Spaces: An Optimization Approach
ICDM '04 Proceedings of the Fourth IEEE International Conference on Data Mining
Mining rank-correlated sets of numerical attributes
Proceedings of the 12th ACM SIGKDD international conference on Knowledge discovery and data mining
Constraint-based concept mining and its application to microarray data analysis
Intelligent Data Analysis
LPD'04 Proceedings of the 2004 international conference on Local Pattern Detection
Constraint-Based mining of fault-tolerant patterns from boolean data
KDID'05 Proceedings of the 4th international conference on Knowledge Discovery in Inductive Databases
Multi-way set enumeration in real-valued tensors
Proceedings of the 2nd Workshop on Data Mining using Matrices and Tensors
Minimum variance associations: discovering relationships in numerical data
PAKDD'08 Proceedings of the 12th Pacific-Asia conference on Advances in knowledge discovery and data mining
Summarizing transactional databases with overlapped hyperrectangles
Data Mining and Knowledge Discovery
Biclustering numerical data in formal concept analysis
ICFCA'11 Proceedings of the 9th international conference on Formal concept analysis
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Thanks to an important research effort the last few years, inductive queries on set patterns and complete solvers which can evaluate them on large 0/1 data sets have been proved extremely useful. However, for many application domains, the raw data is numerical (matrices of real numbers whose dimensions denote objects and properties). Therefore, using efficient 0/1 mining techniques needs for tedious Boolean property encoding phases. This is, e.g., the case, when considering microarray data mining and its impact for knowledge discovery in molecular biology. We consider the possibility to mine directly numerical data to extract collections of relevant bi-sets, i.e., couples of associated sets of objects and attributes which satisfy some user-defined constraints. Not only we propose a new pattern domain but also we introduce a complete solver for computing the so-called numerical bi-sets. Preliminary experimental validation is given.