Mining association rules between sets of items in large databases
SIGMOD '93 Proceedings of the 1993 ACM SIGMOD international conference on Management of data
Fast Algorithms for Mining Association Rules in Large Databases
VLDB '94 Proceedings of the 20th International Conference on Very Large Data Bases
Enhanced mining of association rules from data cubes
DOLAP '06 Proceedings of the 9th ACM international workshop on Data warehousing and OLAP
The Data Warehouse Lifecycle Toolkit
The Data Warehouse Lifecycle Toolkit
Business Intelligence: Data Mining and Optimization for Decision Making
Business Intelligence: Data Mining and Optimization for Decision Making
Ontology-Enhanced association mining
EWMF'05/KDO'05 Proceedings of the 2005 joint international conference on Semantics, Web and Mining
Observational Calculi and Association Rules
Observational Calculi and Association Rules
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The traditional approach to integration of data mining algorithms with OLAP is that of predictive mining applied on transactional data with the aim of explaining the findings manually discovered via OLAP. We propose an alternative model, in which a descriptive mining system operates on data already aggregated for OLAP, namely, on periodic snapshots of observed measures. The proposed solution aims to provide the business analyst with hypotheses about relative frequencies of snapshots satisfying various dimensional values, as guidance for OLAP-based analysis. The rich inventory of hypothesis features in the GUHA data mining method and the efficient processing by the underlying LISp-Miner tool is being exploited for this purpose.