Mining association rules between sets of items in large databases
SIGMOD '93 Proceedings of the 1993 ACM SIGMOD international conference on Management of data
starER: a conceptual model for data warehouse design
Proceedings of the 2nd ACM international workshop on Data warehousing and OLAP
Hierarchy-based mining of association rules in data warehouses
SAC '00 Proceedings of the 2000 ACM symposium on Applied computing - Volume 1
Automatically generating OLAP schemata from conceptual graphical models
Proceedings of the 3rd ACM international workshop on Data warehousing and OLAP
PARSIMONY: An infrastructure for parallel multidimensional analysis and data mining
Journal of Parallel and Distributed Computing - Special issue on high-performance data mining
Common Warehouse Metamodel: An Introduction to the Standard for Data Warehouse Integration
Common Warehouse Metamodel: An Introduction to the Standard for Data Warehouse Integration
A novel three-level architecture for large data warehouses
Journal of Systems Architecture: the EUROMICRO Journal
Ad-Hoc Association-Rule Mining within the Data Warehouse
HICSS '03 Proceedings of the 36th Annual Hawaii International Conference on System Sciences (HICSS'03) - Track 8 - Volume 8
Mining Association Rules from Stars
ICDM '02 Proceedings of the 2002 IEEE International Conference on Data Mining
Mapping nominal values to numbers for effective visualization
Information Visualization - Special issue of selected and extended InfoVis 03 papers
Mining Association Rules from the Star Schema on a Parallel NCR Teradata Database System
ITCC '05 Proceedings of the International Conference on Information Technology: Coding and Computing (ITCC'05) - Volume I - Volume 01
On Mining Summaries by Objective Measures of Interestingness
Machine Learning
Interestingness measures for data mining: A survey
ACM Computing Surveys (CSUR)
Proceedings of the 12th ACM SIGKDD international conference on Knowledge discovery and data mining
ICME '03 Proceedings of the 2003 International Conference on Multimedia and Expo - Volume 3 (ICME '03) - Volume 03
Enhanced mining of association rules from data cubes
DOLAP '06 Proceedings of the 9th ACM international workshop on Data warehousing and OLAP
Lazy Associative Classification
ICDM '06 Proceedings of the Sixth International Conference on Data Mining
A Lazy Approach to Associative Classification
IEEE Transactions on Knowledge and Data Engineering
SAMSTAR: An Automatic Tool for Generating Star Schemas from an Entity-Relationship Diagram
ER '08 Proceedings of the 27th International Conference on Conceptual Modeling
A New Data Cube for Integrating Data Mining and OLAP
ICDEW '07 Proceedings of the 2007 IEEE 23rd International Conference on Data Engineering Workshop
A Conceptual Model for Combining Enhanced OLAP and Data Mining Systems
NCM '09 Proceedings of the 2009 Fifth International Joint Conference on INC, IMS and IDC
Learning task models in ill-defined domain using an hybrid knowledge discovery framework
Knowledge-Based Systems
Latent OLAP: data cubes over latent variables
Proceedings of the 2011 ACM SIGMOD International Conference on Management of data
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
Weighted association rule mining via a graph based connectivity model
Information Sciences: an International Journal
DaWaK'07 Proceedings of the 9th international conference on Data Warehousing and Knowledge Discovery
Association rule mining to detect factors which contribute to heart disease in males and females
Expert Systems with Applications: An International Journal
A data mining approach to knowledge discovery from multidimensional cube structures
Knowledge-Based Systems
Principal component analysis-based control charts for multivariate nonnormal distributions
Expert Systems with Applications: An International Journal
Hi-index | 12.05 |
The integration of data mining techniques with data warehousing is gaining popularity due to the fact that both disciplines complement each other in extracting knowledge from large datasets. However, the majority of approaches focus on applying data mining as a front end technology to mine data warehouses. Surprisingly, little progress has been made in incorporating mining techniques in the design of data warehouses. While methods such as data clustering applied on multidimensional data have been shown to enhance the knowledge discovery process, a number of fundamental issues remain unresolved with respect to the design of multidimensional schema. These relate to automated support for the selection of informative dimension and fact variables in high dimensional and data intensive environments, an activity which may challenge the capabilities of human designers on account of the sheer scale of data volume and variables involved. In this research, we propose a methodology that selects a subset of informative dimension and fact variables from an initial set of candidates. Our experimental results conducted on three real world datasets taken from the UCI machine learning repository show that the knowledge discovered from the schema that we generated was more diverse and informative than the standard approach of mining the original data without the use of our multidimensional structure imposed on it.