Most clustering methods focus on numerical data. However, most data existing in databases are both categorical and numerical. To date, clustering methods have been developed to analyze only complete data. Although we sometimes encounter data sets that contain one or more missing feature values (incomplete data), traditional clustering methods cannot be used for such data. Thus, we study this theme and discuss clustering methods that can handle mixed numerical and categorical incomplete data. In this paper, we propose an algorithm that uses the missing categorical data imputation method and distances between numerical data that contain missing values. Furthermore, we apply fuzzy clustering for interpreting results that are vague.
