The design of relational databases
The design of relational databases
Identifying the Minimal Transversals of a Hypergraph and Related Problems
SIAM Journal on Computing
Data mining, hypergraph transversals, and machine learning (extended abstract)
PODS '97 Proceedings of the sixteenth ACM SIGACT-SIGMOD-SIGART symposium on Principles of database systems
Generating non-redundant association rules
Proceedings of the sixth ACM SIGKDD international conference on Knowledge discovery and data mining
Data mining: concepts and techniques
Data mining: concepts and techniques
Efficient computation of Iceberg cubes with complex measures
SIGMOD '01 Proceedings of the 2001 ACM SIGMOD international conference on Management of data
Machine Learning
Formal Concept Analysis: Mathematical Foundations
Formal Concept Analysis: Mathematical Foundations
Constrained frequent pattern mining: a pattern-growth view
ACM SIGKDD Explorations Newsletter
Searching for dependencies at multiple abstraction levels
ACM Transactions on Database Systems (TODS)
Data Cube: A Relational Aggregation Operator Generalizing Group-By, Cross-Tab, and Sub-Totals
Data Mining and Knowledge Discovery
Levelwise Search and Borders of Theories in KnowledgeDiscovery
Data Mining and Knowledge Discovery
Computing iceberg concept lattices with TITANIC
Data & Knowledge Engineering
Efficient Mining of Intertransaction Association Rules
IEEE Transactions on Knowledge and Data Engineering
Using Lattice-Based Framework as a Tool for Feature Extraction
ECML '98 Proceedings of the 10th European Conference on Machine Learning
Decision Tables: Scalable Classification Exploring RDBMS Capabilities
VLDB '00 Proceedings of the 26th International Conference on Very Large Data Bases
Mining Multi-Dimensional Constrained Gradients in Data Cubes
Proceedings of the 27th International Conference on Very Large Data Bases
Condensed Cube: An Efficient Approach to Reducing Data Cube Size
ICDE '02 Proceedings of the 18th International Conference on Data Engineering
Quotient cube: how to summarize the semantics of a data cube
VLDB '02 Proceedings of the 28th international conference on Very Large Data Bases
Upper Borders for Emerging Cubes
DaWaK '08 Proceedings of the 10th international conference on Data Warehousing and Knowledge Discovery
Emerging Cubes: Borders, size estimations and lossless reductions
Information Systems
Reduced representations of Emerging Cubes for OLAP database mining
International Journal of Business Intelligence and Data Mining
Extracting semantics in OLAP databases using emerging cubes
Information Sciences: an International Journal
The agree concept lattice for multidimensional database analysis
ICFCA'11 Proceedings of the 9th international conference on Formal concept analysis
ICFCA'10 Proceedings of the 8th international conference on Formal Concept Analysis
Lossless reduction of datacubes
DEXA'06 Proceedings of the 17th international conference on Database and Expert Systems Applications
Towards mining frequent queries in star schemes
KDID'05 Proceedings of the 4th international conference on Knowledge Discovery in Inductive Databases
Using functional dependencies for reducing the size of a data cube
FoIKS'12 Proceedings of the 7th international conference on Foundations of Information and Knowledge Systems
Emerging cubes for trends analysis in OLAP databases
DaWaK'07 Proceedings of the 9th international conference on Data Warehousing and Knowledge Discovery
Constrained Cube Lattices for Multidimensional Database Mining
International Journal of Data Warehousing and Mining
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In this paper we propose a lattice-based approach intended for extracting semantics from datacubes: borders of version spaces for supervised classification, closed cube lattice to summarize the semantics of datacubes w.r.t. COUNT, SUM, and covering graph of the quotient cube as a visualization tool of minimal multidimensional associations. With this intention, we introduce two novel concepts: the cube transversals and the cube closures over the cube lattice of a categorical database relation. We propose a levelwise merging algorithm for mining minimal cube transversals with a single database scan. We introduce the cube connection, show that it is a Galois connection and derive a closure operator over the cube lattice. Using cube transversals and closures, we define a new characterization of boundary sets which provide a condensed representation of version spaces used to enhance supervised classification. The algorithm designed for computing such borders improves the complexity of previous proposals. We also introduce the concept of closed cube lattice and show that it is isomorph to on one hand the Galois lattice and on the other hand the quotient cube w.r.t. COUNT, SUM. Proposed in [16], the quotient cube is a succinct summary of a datacube preserving the Rollup/Drilldown semantics. We show that the quotient cube w.r.t. COUNT, SUM and the closed cube lattice have a similar expression power but the latter has the smallest possible size. Finally we focus on the multidimensional association issue and introduce the covering graph of the quotient cube which provides the user with a visualization tool of minimal multidimensional associations.