Special Issue on Computational Intelligence in Data mining

In our society the amount of data doubles almost every year. Hence, there is an urgent need for a new generation of computationally intelligent techniques and tools to assist humans in extracting useful information (knowledge) from the rapidly growing volume of data. 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 sys-tem's behavior. Therefore, we need an approximate reasoning system capable of handling such imperfect information. While Bezdek [2] defines such approaches within a frame called computational intelligence, Zadeh [3] 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 provide 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 [4]. This special issue deals with the importance of computational intelligence (CI) paradigms in data mining and knowledge discovery. The first paper is aimed to give a comprehensive view about the links between computational intelligence and data mining. Further, a case study is also given in which the extracted knowledge is represented by fuzzy rule-based expert systems obtained by soft computing based data mining algorithms. It is recognized that both model performance and interpretability are of major importance, and effort is required to keep the resulting rule bases small and com-prehensible. Therefore, CI technique based data mining algorithms have been developed for feature selection, feature extraction, model optimization and model reduction (rule base simplification). The results illustrate that that CI based tools can be applied in a synergistic manner though the nine steps of knowledge discovery. The remaining papers were selected from the papers presented at the …