What is Dynamic Causal Mining

It is an iterative and continual process of mining rules, formulating policies, testing, and revising models. The stages are as follows: Stage 1: Problem definition. In this phase, the problem is identified and the key variables are issued. Also, the time horizon is defined so that the cause and effects can be identified, Stage 2: Data preparation. Data are collected from various sources and a homogeneous data source is created to eliminate the representation and encoding differences, Stage 3: Data mining. This stage involves transforming data into rules by applying data mining tools, Stage 4: Policy formulation. Policies are groups of the rules extracted by mining techniques. Policies improve the understanding of the system. The interactions of different policies must also be considered since the impact of combined policies is usually not the sum of their impacts alone. These interactions may reinforce or have an opposite effect. The policy can be used for behaviour simulation to predict the future outcome, Stage 5: Model simulation. This stage tests the accuracy of the policies. The policies will predict results for new cases so the managers can alter the policy to improve future behaviour of the system. It is necessary to capture the appropriate data and generate a prediction in real time so that a decision can be made directly, quickly, and accurately.

Applying Fuzzy Logic in Dynamic Causal Mining

Yi Wang (Cardiff University, UK)