Comparative Study of Association Rule Mining and MiSTIC in Extracting Spatio-temporal Disease Occurrences Patterns

Abstract

Extracting interesting and useful patterns from spatio-temporal datasets is more difficult than extracting the corresponding patterns from traditional numeric and categorical data due to the complexity of spatial data types and the embedded topologies, spatial and temporal relationships, and spatial autocorrelation. The objective of epidemiology is to identify disease causes and correlating them to spatially explicit disease patterns and variations in health risks. The main issue in traditional mining of association rules in disease surveillance data is that a large number of rules are discovered, but most of them are of limited use in addressing the stated objectives or original questions asked. Moreover, not all of the generated rules are interesting (due to their inability to conclusively mine spatio-temporal prevalence and causative factors of diseases), and some rules may be ignored. These drawbacks result as these methods ignore the inherent spatiotemporal dependency in such data. This paper makes a case for the use of MiSTIC algorithm to address these issues, compare the use of traditional association rule mining in context of Salmonellosis disease management, and share new insights. An illustrative case study presented here suggests that in comparison to traditional association rule mining, even simple spatio-temporal data mining approaches taking into consideration the spatio-temporal interdependencies in disease data, can provide new and valuable scientific insights towards efficient disease surveillance and management.

Big data analytics for preventive medicine

“…HAPBM is a variant of Apriori algorithm with difference in conversion of discretized dataset into Boolean matrix, and then frequent itemset generation from boolean matrix. Raheja and Rajan [185] comparatively analyzed ARM and MiSTIC (Mining Spatio-Temporally Invariant Core Regions) approaches for extracting spatio-temporal of Salmonellosis disease occurrence pattern. Apriori algorithm is used for ARM with minimum support and minimum confidence values of 1 and 40 percent respectively.…”

Frequent Pattern Mining on Time and Location Aware Air Quality Data

“…Spatio-temporal association rule mining is used for various applications in the past. Such as for mining disease [3], road networks [4] and mining bus Id card databases [5], marine environments [6], traffic [7] etc.…”

Global Land Surface Dry/Wet Conditions Mining Based on Spatial‐Temporal Association Rules

“…He et al, 2020). Spatial-temporal data mining methods refer to analytic processes designed to search for consistent patterns and/or systematic relationships between variables from large volumes of spatial-temporal data (Jain & Srivastava, 2013;Xie et al, 2018), which are not used to prove or disprove preexisting hypotheses but rather to identify patterns embedded within spatial-temporal data (Mennis & Liu, 2005;Raheja & Rajan, 2012;Xu et al, 2017). As one of the most important spatial-temporal data mining tasks (Agrawal & Srikant, 1994;Schlüter & Conrad, 2010), spatial-temporal association rule mining is expected to discover the presence of pair conjunctions appearing in a spatial-temporal data set (Qin et al, 2015),which has the advantage to discover nontrivial, implicit, previous unknown, but potentially useful knowledge from large data sets and can be used to mine the spatial-temporal associations of dry/wet conditions in meteorological data (Han et al, 2011;Laube et al, 2008;Pei et al, 2020;Xue et al, 2014).…”