Bottom-up computation of sparse and Iceberg CUBE
SIGMOD '99 Proceedings of the 1999 ACM SIGMOD international conference on Management of data
Efficient mining of association rules using closed itemset lattices
Information Systems
Proceedings of the twenty-first ACM SIGMOD-SIGACT-SIGART symposium on Principles of database systems
Data Cube: A Relational Aggregation Operator Generalizing Group-By, Cross-Tab, and Sub-Totals
Data Mining and Knowledge Discovery
Computing iceberg concept lattices with TITANIC
Data & Knowledge Engineering
Extracting semantics from data cubes using cube transversals and closures
Proceedings of the ninth ACM SIGKDD international conference on Knowledge discovery and data mining
Pushing Convertible Constraints in Frequent Itemset Mining
Data Mining and Knowledge Discovery
On Closed Constrained Frequent Pattern Mining
ICDM '04 Proceedings of the Fourth IEEE International Conference on Data Mining
Efficient Algorithms for Mining Closed Itemsets and Their Lattice Structure
IEEE Transactions on Knowledge and Data Engineering
Mining border descriptions of emerging patterns from dataset pairs
Knowledge and Information Systems
Data Mining: Concepts and Techniques
Data Mining: Concepts and Techniques
C-Cubing: Efficient Computation of Closed Cubes by Aggregation-Based Checking
ICDE '06 Proceedings of the 22nd International Conference on Data Engineering
CURE for cubes: cubing using a ROLAP engine
VLDB '06 Proceedings of the 32nd international conference on Very large data bases
Computing Iceberg Cubes by Top-Down and Bottom-Up Integration: The StarCubing Approach
IEEE Transactions on Knowledge and Data Engineering
Efficient Computation of Iceberg Cubes by Bounding Aggregate Functions
IEEE Transactions on Knowledge and 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
Supporting the data cube lifecycle: the power of ROLAP
The VLDB Journal — The International Journal on Very Large Data Bases
The levelwise version space algorithm and its application to molecular fragment finding
IJCAI'01 Proceedings of the 17th international joint conference on Artificial intelligence - Volume 2
Emerging cubes for trends analysis in OLAP databases
DaWaK'07 Proceedings of the 9th international conference on Data Warehousing and Knowledge Discovery
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
Effective data warehouse for information delivery: a literature survey and classification
International Journal of Networking and Virtual Organisations
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In this paper, we investigate reduced representations for the Emerging Cube. We use the borders, classical in data mining, for the Emerging Cube. These borders can support classification tasks to know whether a trend is emerging or not. However, the borders do not make possible to retrieve the measure values. This is why we introduce two new and reduced representations without measure loss: the L-Emerging Closed Cube and Emerging Quotient Cube. We state the relationship between the introduced representations. Experiments performed on various data sets are intended to measure the size of the three reduced representations.