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Norma: A Framework for Finding Threshold Associations Between Continuous Variables Using Point-wise Functions
  • Md Mahin ,
  • Christoph F. Eick
Md Mahin
University of Houston

Corresponding Author:[email protected]

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Christoph F. Eick
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This study introduces Norma, a novel association-mining framework tailored for continuous spatial variables analysis. Norma introduces the unique Continuous Variable Threshold (CVT) pattern, aiming to identify a pair of thresholds within the value domain of two continuous variables, revealing strong associations within a specified geographic area. For example, it may unveil a strong association between COVID-19 infection rates above 2% and poverty rates above 15% in New Mexico. Norma associates pointwise functions with each variable-e.g., a function that returns poverty rates for each location in New Mexico. It employs a novel interestingness function, which measures agreement with respect to hotspots where variable pointwise functions exceed associated thresholds. Norma also employs a grid-based spatial hotspot-growing algorithm to discover high-interestingness regions and pairs of thresholds that generate interestingness surpassing a predefined threshold. Furthermore, the framework introduces measures for assessing variable relatedness based on CVT associations. A comparative case study against traditional correlation methods are presented using county-level COVID-19 infection rates and nineteen other socio-economic variables from the continuous United States, and demonstrate how Norma can be used to explore association among subset of values related to spatial continuous variables.