Measuring interestingness of discovered skewed patterns in data cubes

Abstract

a b s t r a c t a r t i c l e i n f oThis paper describes a methodology of OLAP cube navigation to identify interesting surprises by using a skewness based approach. Three different measures of interestingness of navigation rules are proposed. The navigation rules are examined for their interestingness in terms of their expectedness of skewness from neighborhood rules. A novel Axis Shift Theory (AST) to determine interesting navigation paths is presented along with an attribute influence approach for generalization of rules, which measures the interestingness of dimensional attributes and their relative influence on navigation paths. Detailed examples and extensive experiments demonstrate the effectiveness of interestingness of navigation rules.Published by Elsevier B.V.

A Framework for Learning Cell Interestingness from Cube Explorations

“…Skewness is computed observing the underlying facts (the raw data that is aggregated), looking for outliers or substantial differences with other facts. Based on skewness, Kumar et al [18] propose interestingness measures based on the unexpectedness of skewness in navigation rules and navigation paths.…”

A Subjective Interestingness measure for Business Intelligence explorations

“…In the context of data cube exploration, to the best of our knowledge there is no final and consensual interestingness measure or belief distribution elicitation method, while there exists measures that are closely related. Measures have been defined as unexpectedness of skewness in navigation rules and navigation paths [20] and computed as a peculiarity measure of asymmetry in data distribution [18]. In [14], the authors define interestingness measures in a data cube as a difference between expected and observed probability for each attribute-value pair and the the degree of correlation among two attributes.…”

Measuring Diversity of Associations Rules Extracted from A Data Warehouse

“…As an alternate, the authors in [1] worked on indentifying interesting patterns by adapting a skewness based approach. Authors investigated three measures of interestingness for navigation rules.…”

“…In some of previous studies the patterns are evaluated using statistical approaches [1], [2]. In some of the techniques, authors have relied on conventional measures like Support and Count etc.…”