Applying Fuzzy Logic in Dynamic Causal Mining

Dynamic causal mining (DCM) assists decision makers in controlling a system at decision points by converting data into policies. DCM searches for simultaneous dynamic causal relations in a database and discovers delay and feedback relationships between attributes based on separate time stamps. This makes the algorithm more suitable for dynamic modeling and enables the discovery of hidden dynamic structures, which can be applied to predict the future behaviour of a dynamic system. Causality, in this chapter, denotes a relationship between two or more entities. There are two types of causality: static and dynamic. In marked basket analysis, an example of static causality could be that purchasing nails might cause the purchase of a hammer. An example of dynamic causality is that an increase in the purchase of chips might cause an increase in the purchase of soft drinks. However, an increase in the purchase of nails might not cause an increase in the purchase of hammers (one hammer is enough to nail all the nails), thus this is not a dynamic causality. AbstrAct