Discovering Interesting Association Rules from Legal Databases

Abstract

The Knowledge Discovery from Databases (KDD) technique called 'association rules' is applied to a large data set representing applicants for government-funded legal aid. Results indicate that KDD can be an invaluable tool for legal analysts. Association rules discovered identify associations between variables that are present in the data set though are not necessarily causal. Interesting rules can prompt analysts to formulate hypotheses for further investigation. The identi cation of interesting rules is typically performed using an objective measure of 'interesting' although this measure is often not suf ciently accurate to eliminate all uninteresting rules. In this article, a subjective measure of interestingness is adopted in conjunction with the objective measures. This leads to the ability to focus more accurately on those rules that surprise the analyst and are therefore more likely to be interesting. In general, KDD techniques have not been applied to law despite possible bene ts because data is often stored in narrative form rather than in structured databases. However, the impending introduction of data warehouses that collect data from a number of organizations across a legal system presents invaluable opportunities for analysts using KDD.

Using Market Basket Analysis in Management Research

“…These successful applications of MBA in other scientific fields are good indicators of the potential of using MBA in management research. For example, MBA has been used to address questions of theoretical and practical importance in finance, telecommunications, and web analysis (Y.-L. Chen et al, 2005); geophysics (Yang et al, 2007); and legal aid services (Ivkovic, Yearwood, & Stranieri, 2002). Moreover, MBA has been used in cross-disciplinary research examining associations among ocean, land, and atmospheric processes (Tan, Steinbach, & Kumar, 2005).…”