Host: Japan Society for Fuzzy Theory and Intelligent Informatics (SOFT)
Most clustering methods focus on numerical data. However, lots of the data existed in the databases are both categorical and numerical. Until now, clustering methods have been developed to analyze complete data. Although we sometimes encounter data sets which contain missing one or more feature values (incomplete data), traditional clustering methods cannot be used for these kinds of data. Therefore, we study this problem and discuss clustering methods which can handle mixed numerical and categorical incomplete data. Further, we apply fuzzy clustering for interpreting the result with vagueness.
