Abstract
This chapter applies fuzzy logic to a dynamic causal mining (DCM) algorithm and argues that DCM, a combination of association mining and system dynamics for discovering causality patterns, needs a potentially more substantive approach for the user to understand the nature of extracted rules and information in a variety of contexts. Furthermore, the author hopes that the use of fuzzy logic will not only assist the user to make better decisions, but also assist in a better understanding of future behaviour of the target system.
Key Terms in this Chapter
Sympathetic Rule: It causes an increase or decrease in the output of a target system. It reinforces a change with more change in the same direction.
Dynamic Causal Rule: A dynamic causal rule consists of variables connected by arrows denoting the causal influences among the attributes. Two attributes A1 and A2 are linked by a causal arrow. Each causal link is assigned a polarity, and the link indicates the direction of the change. A dynamic causal rule is derived from a frequent dynamic attribute set.
Association Mining: It is the discovery of patterns discovered in data that includes the concepts of transaction, basket, and group. A common example of a transaction is the set of items someone buys during a supermarket trip. Not all data sets have a transaction-based structure, for example, a database of names, ages, and addresses includes no obvious transactions and will not yield association rules (http://www.wikipedia.com).
Polarity: It indicates the direction of a change of an attribute. There are three types of polarity (+, -, 0); + indicates an increase, - indicates a decrease, and 0 indicates neutrality, that is, no change at all.
System Dynamics: System dynamics is an approach to understanding the behaviour of a target system over time. It deals with internal feedback loops and time delays that affect the behaviour of the entire system. Computer software is used to simulate a system dynamics model of the situation being studied. Running what-if simulations to test certain policies on such a model can greatly aid in understanding how the system changes over time. System dynamics is very similar to systems thinking and constructs the same causal loop diagrams of systems with feedback (http://www.wikipedia.com).
Antipathetic Rule: An antipathetic rule represents an adjustment to achieve a certain goal or objective. It indicates a system attempting to change from its current state to a goal state. This implies that if the current state is above the goal state, then the system forces it down. If the current state is below the goal state, the system pushes it up. An antipathetic rule provides useful stability but resists external changes.
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.
System Thinking: It is an approach to analysis that is based on the belief that the component parts of a system will act differently when isolated from their environment or other parts of the system. Because the whole is greater than the sum of its parts (the relationship between the parts is what should be under observation), any atomistic analysis is considered reductionist. Standing in contrast to Descartes’ and others’ reductionism, it proposes to view systems in a holistic manner (http://www.wikipedia.com).