Mining association rules between sets of items in large databases
SIGMOD '93 Proceedings of the 1993 ACM SIGMOD international conference on Management of data
An overview of data warehousing and OLAP technology
ACM SIGMOD Record
Machine Learning
Fundamentals of Database Systems
Fundamentals of Database Systems
Discovery-Driven Exploration of OLAP Data Cubes
EDBT '98 Proceedings of the 6th International Conference on Extending Database Technology: Advances in Database Technology
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
Analyzing and Predicting Images Through a Neural Network Approach
VBC '96 Proceedings of the 4th International Conference on Visualization in Biomedical Computing
Exploratory medical knowledge discovery: experiences and issues
ACM SIGKDD Explorations Newsletter
Data Mining: Concepts and Techniques
Data Mining: Concepts and Techniques
Rule interestingness analysis using OLAP operations
Proceedings of the 12th ACM SIGKDD international conference on Knowledge discovery and data mining
Constraining and summarizing association rules in medical data
Knowledge and Information Systems
Association rule discovery with the train and test approach for heart disease prediction
IEEE Transactions on Information Technology in Biomedicine
OLAP with UDFs in digital libraries
Proceedings of the 18th ACM conference on Information and knowledge management
Fast UDFs to compute sufficient statistics on large data sets exploiting caching and sampling
Data & Knowledge Engineering
Query recommendation in digital libraries using OLAP
Proceedings of the 2nd International Workshop on Keyword Search on Structured Data
OLAP-based query recommendation
CIKM '10 Proceedings of the 19th ACM international conference on Information and knowledge management
Efficient algorithms based on relational queries to mine frequent graphs
PIKM '10 Proceedings of the 3rd workshop on Ph.D. students in information and knowledge management
Repairing OLAP queries in databases with referential integrity errors
DOLAP '10 Proceedings of the ACM 13th international workshop on Data warehousing and OLAP
Evaluating association rules and decision trees to predict multiple target attributes
Intelligent Data Analysis
Interactive exploration and visualization of OLAP cubes
Proceedings of the ACM 14th international workshop on Data Warehousing and OLAP
A data mining approach to knowledge discovery from multidimensional cube structures
Knowledge-Based Systems
Query Recommendations for OLAP Discovery-Driven Analysis
International Journal of Data Warehousing and Mining
Optimizing OLAP cube processing on solid state drives
Proceedings of the sixteenth international workshop on Data warehousing and OLAP
Discovering frequent pattern pairs
Intelligent Data Analysis
Hi-index | 0.00 |
Statistical tests represent an important technique used to formulate and validate hypotheses on a dataset. They are particularly useful in the medical domain, where hypotheses link disease with medical measurements, risk factors, and treatment. In this paper, we propose to compute parametric statistical tests treating patient records as elements in a multidimensional cube. We introduce a technique that combines dimension lattice traversal and statistical tests to discover significant differences in the degree of disease within pairs of patient groups. In order to understand a cause-effect relationship, we focus on patient group pairs differing in one dimension. We introduce several optimizations to prune the search space, to discover significant group pairs, and to summarize results. We present experiments showing important medical findings and evaluating scalability with medical datasets.