Mining The Successful Binary Combinations: Methodology and A Simple Case Study

TL;DR

This paper introduces a four-phase methodology for analyzing dichotomous data to identify simple, effective rules that predict success or failure across various fields, using Boolean function minimization.

Contribution

It presents a novel approach combining data transformation, Boolean minimization, and rule filtering to extract simple predictive rules from binary datasets.

Findings

Effective rules involving one or two features are identified.

The methodology is applicable to diverse success/failure analysis areas.

Simplified rules enhance interpretability and decision-making.

Abstract

The importance of finding the characteristics leading to either a success or a failure is one of the driving forces of data mining. The various application areas of finding success/failure factors cover vast variety of areas such as credit risk evaluation and granting loans, micro array analysis, health factors and health risk factors, and parameter combination leading to a product success. This paper presents a new approach for making inferences about dichotomous data. The objective is to determine rules that lead to a certain result. The method consists of four phases: in the first phase, the data is processed into a binary format of a truth table, in the second phase; rules are found by utilizing an algorithm that minimizes Boolean functions. In the third phase the rules are checked and filtered. In the fourth phase, simple rules that involve one to two features are revealed.