Knowledge processing with interval methods has intrinsic merit. First, qualitative properties are often presented as ranges of data attributes rather than specific points. For example, one’s blood pressure is normal if within the normal range (i. e. normal interval). By grouping attribute values into meaningful intervals, we can omit insignificant quantitative differences and focus more on qualitatively processing datasets. More importantly, interval-valued attributes contain more information than points and can represent variability and uncertainty. Finally, interval-valued computational results can be more meaningful and useful than point-valued output in a dynamic environment.
Statistical and probabilistic methods have been widely applied in knowledge discovery. However, despite the fact that confidence intervals and fuzzy intervals have been used to deal with uncertainties, they may not always work well in practice. By integrating interval methods with stochastic models and fuzzy logic, this book provides at least additional, if not more powerful, tools for knowledge processing, especially for handling variability and uncertainty.