Qualified Association-Rule Mining - Expanding Public Safety Data Insight

摘要

Data mining is a process whose objective is to identify valid, novel, potentially useful and understandable correlations and patterns in existing data, using a broad spectrum of formalisms and techniques. Data mining is increasingly being used in areas such as homeland security, public safety and criminal justice. This paper brings attention to a novel data-mining framework, called qualified association rules, as a useful method for expanding public safety-related insight from the analytical data repositories. Qualified association rules mining is a generalization of the widely applied association rules data mining method. This paper uses a public safety scenario related to a retail example of a drugstore chain to demonstrate how qualified association rules mining can provide more insight from the data than association rules mining alone.