Data Mining with Fuzzy Methods: Status and Perspectives

Data mining is the central step in a process called knowledge discovery in databases, namely the step in which modeling techniques are applied. Several research areas like statistics, artificial intelligence, machine learning, and soft computing have contributed to its arsenal of methods. In this paper, however, we focus on fuzzy methods for rule learning, information fusion, and dependency analysis. In our opinion fuzzy approaches can play an important role in data mining, because they provide comprehensible results (although this goal is often neglected—maybe because it is sometimes hard to achieve with other methods). In addition, the approaches studied in data mining have mainly been oriented at highly structured and precise data. However, we expect that the analysis of more complex heterogeneous information source like texts, images, rule bases etc. will become more important in the near future. Therefore we give an outlook on information mining, which we see as an extension of data mining to treat complex heterogeneous information sources, and argue that fuzzy systems are useful in meeting the challenges of information mining.