The KDD process for extracting useful knowledge from volumes of data
Communications of the ACM
Towards on-line analytical mining in large databases
ACM SIGMOD Record
PARSIMONY: An infrastructure for parallel multidimensional analysis and data mining
Journal of Parallel and Distributed Computing - Special issue on high-performance data mining
iDiff: Informative Summarization of Differences in Multidimensional Aggregates
Data Mining and Knowledge Discovery
Unsupervised Learning with Mixed Numeric and Nominal Data
IEEE Transactions on Knowledge and Data Engineering
IEEE Transactions on Visualization and Computer Graphics
ICME '03 Proceedings of the 2003 International Conference on Multimedia and Expo - Volume 3 (ICME '03) - Volume 03
A k-mean clustering algorithm for mixed numeric and categorical data
Data & Knowledge Engineering
Multiple factor analysis and clustering of a mixture of quantitative, categorical and frequency data
Computational Statistics & Data Analysis
Incremental clustering of mixed data based on distance hierarchy
Expert Systems with Applications: An International Journal
Data Warehousing and Mining: Concepts, Methodologies, Tools, and Applications
Data Warehousing and Mining: Concepts, Methodologies, Tools, and Applications
Evaluating statistical tests on OLAP cubes to compare degree of disease
IEEE Transactions on Information Technology in Biomedicine - Special section on computational intelligence in medical systems
Learning task models in ill-defined domain using an hybrid knowledge discovery framework
Knowledge-Based Systems
Expert Systems with Applications: An International Journal
Clustering mixed data based on evidence accumulation
ADMA'06 Proceedings of the Second international conference on Advanced Data Mining and Applications
A fuzzy k-prototype clustering algorithm for mixed numeric and categorical data
Knowledge-Based Systems
DaWaK'07 Proceedings of the 9th international conference on Data Warehousing and Knowledge Discovery
Automatic Item Weight Generation for Pattern Mining and its Application
International Journal of Data Warehousing and Mining
Discovering diverse association rules from multidimensional schema
Expert Systems with Applications: An International Journal
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In this research we present a novel methodology for the discovery of cubes of interest in large multi-dimensional datasets. Unlike previous research in this area, our approach does not rely on the availability of specialized domain knowledge and instead makes use of robust methods of data reduction such as Principal Component Analysis and Multiple Correspondence Analysis to identify a small subset of numeric and nominal variables that are responsible for capturing the greatest degree of variation in the data and are thus used in generating cubes of interest. Hierarchical clustering was integrated with the use of data reduction in order to gain insights into the dynamics of relationships between variables of interests at different levels of data abstraction. The two case studies that were conducted on two real word datasets revealed that the methodology was able to capture regions of interest that were significant from both the application and statistical perspectives.