Mining association rules between sets of items in large databases
SIGMOD '93 Proceedings of the 1993 ACM SIGMOD international conference on Management of data
Implementing data cubes efficiently
SIGMOD '96 Proceedings of the 1996 ACM SIGMOD international conference on Management of data
The KDD process for extracting useful knowledge from volumes of data
Communications of the ACM
An overview of data warehousing and OLAP technology
ACM SIGMOD Record
Data mining: concepts and techniques
Data mining: concepts and techniques
CubiST: a new algorithm for improving the performance of ad-hoc OLAP queries
Proceedings of the 3rd ACM international workshop on Data warehousing and OLAP
Machine Learning
Discovery-Driven Exploration of OLAP Data Cubes
EDBT '98 Proceedings of the 6th International Conference on Extending Database Technology: Advances in Database Technology
ICDE '97 Proceedings of the Thirteenth International Conference on Data Engineering
Data Cube: A Relational Aggregation Operator Generalizing Group-By, Cross-Tab, and Sub-Total
ICDE '96 Proceedings of the Twelfth International Conference on Data Engineering
Mining Constrained Association Rules to Predict Heart Disease
ICDM '01 Proceedings of the 2001 IEEE International Conference on Data Mining
On the Computation of Multidimensional Aggregates
VLDB '96 Proceedings of the 22th International Conference on Very Large Data Bases
Analyzing and Predicting Images Through a Neural Network Approach
VBC '96 Proceedings of the 4th International Conference on Visualization in Biomedical Computing
Advanced visualization for OLAP
DOLAP '03 Proceedings of the 6th ACM international workshop on Data warehousing and OLAP
Interactive Visual Exploration of Multidimensional Data: Requirements for CommonGIS with OLAP
DEXA '04 Proceedings of the Database and Expert Systems Applications, 15th International Workshop
Constraining and summarizing association rules in medical data
Knowledge and Information Systems
From analysis to interactive exploration: building visual hierarchies from OLAP cubes
EDBT'06 Proceedings of the 10th international conference on Advances in Database Technology
Association rule discovery with the train and test approach for heart disease prediction
IEEE Transactions on Information Technology in Biomedicine
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In On-Line Analytical Processing (OLAP), users explore a database cube with roll-up and drill-down operations in order to find interesting results. Most approaches rely on simple aggregations and value comparisons in order to validate findings. In this work, we propose to combine OLAP dimension lattice traversal and statistical tests to discover significant metric differences between highly similar groups. A parametric statistical test allows pair-wise comparison of neighboring cells in cuboids, providing statistical evidence about the validity of findings. We introduce a two-dimensional checkerboard visualization of the cube that allows interactive exploration to understand significant measure differences between two cuboids differing in one dimension along with associated image data. Our system is tightly integrated into a relational DBMS, by dynamically generating SQL code, which incorporates several optimizations to efficiently explore the cube, to visualize discovered cell pairs and to view associated images. We present an experimental evaluation with medical data sets focusing on finding significant relationships between risk factors and disease.