Special Issue on Computational Intelligence in Data mining

When we attempt to solve real-world problems, like extracting knowledge from large amount of data, we realize that they are typically ill-defined systems, difficult to model and with large-scale solution spaces. In these cases, precise models are impractical, too expensive, or non-existent. Furthermore, the relevant available information is usually in the form of empirical prior knowledge and input-output data representing instances of the system's behavior. Therefore, we need an approximate reasoning system capable of handling such imperfect information. While Bezdek [1] defines such approaches within a frame called computational intelligence, Zadeh [2] explains the same using the soft computing paradigm. According to Zadeh "... in contrast to traditional, hard computing, soft computing is tolerant of imprecision, uncertainty, and partial truth." In this context Fuzzy Logic (FL), Probabilistic Reasoning (PR), Neural Networks (NNs), and Evolutionary Algorithms (EAs) are considered as main components of CI. Each of these technologies provides us with complementary reasoning and searching methods to solve complex, real-world problems. What is important to note is that soft computing is not a melange. Rather, it is a partnership in which each of the partners contributes a distinct methodology for addressing problems in its domain. In this perspective, the principal constituent methodologies in CI are complementary rather than competitive [3]. This special issue deals with the importance of computational intelligence (CI) paradigms in data mining and knowledge discovery.