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
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
Levelwise Search and Borders of Theories in KnowledgeDiscovery
Data Mining and Knowledge Discovery
Cubegrades: Generalizing Association Rules
Data Mining and Knowledge Discovery
MAFIA: A Maximal Frequent Itemset Algorithm for Transactional Databases
Proceedings of the 17th International Conference on Data Engineering
Efficiently Mining Maximal Frequent Itemsets
ICDM '01 Proceedings of the 2001 IEEE International Conference on Data Mining
Explaining Differences in Multidimensional Aggregates
VLDB '99 Proceedings of the 25th International Conference on Very Large Data Bases
Extracting semantics from data cubes using cube transversals and closures
Proceedings of the ninth ACM SIGKDD international conference on Knowledge discovery and data mining
Mining border descriptions of emerging patterns from dataset pairs
Knowledge and Information Systems
A Thorough Experimental Study of Datasets for Frequent Itemsets
ICDM '05 Proceedings of the Fifth IEEE International Conference on Data Mining
Emerging cubes for trends analysis in OLAP databases
DaWaK'07 Proceedings of the 9th international conference on Data Warehousing and Knowledge Discovery
Convex cube: towards a unified structure for multidimensional databases
DEXA'07 Proceedings of the 18th international conference on Database and Expert Systems Applications
Extracting semantics in OLAP databases using emerging cubes
Information Sciences: an International Journal
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The emerging cube computed from two relations r1, r2of categorical attributes gather the tuples for which the measure value strongly increases from r1to r2. In this paper, we are interested in borders for emerging cubes which optimize both storage space and computation time. Such borders also provide classification and cube navigation tools. Firstly we study the condensed representation through the classical borders Lower / Upper, then we propose the borders Upper*/ Uppermore reduced than the previous ones. We soundly state the connexion between the two representations by using cube transversals. Finally, we perform experiments about the size of the introduced representations. The results are convincing and reinforce our idea that the proposed borders are relevant candidates to be the smallest condensed representation of emerging cubes and thus can be really interesting for trend analysis in Olapdatabases.