Discovering diverse association rules from multidimensional schema.

Highlights: [•] We propose a knowledge discovery methodology to discover diverse association rules. [•] We utilize machine learning and statistical techniques for designing schema. [•] We identify and rank the most informative dimensions present high dimensional schema. [•] We extract informative data cubes at different levels of data abstraction. [•] We perform case studies on three real-world datasets to validate our methodology. [ABSTRACT FROM AUTHOR]