Abstract
In companies, market research is conducted in order to formulate a marketing strategy, and they often carry out a questionnaire to consumers. In conventional analysis of questionnaire data, the subjects are classified based on their attributes such as gender, age and so on, then the averaged data of the classes are used for analysis or comparison. However, sales patterns have been influenced by
individuality rather than the attributes recently, and using averaged data has possibility of losing individuality in data. So, it is difficult for companies to obtain effective results by conventional analysis
methods. The authors have developed clustering and analysis methods for questionnaire data according
to individuality with an assumption that the individualities of subjects can be found in the answered
data. In addition, the authors have defined "Interest Degree" as one of the individualities which shows
the degree of interest for evaluation objects and proposed the filtering and clustering methods based on
that. This paper studies the clustering method using Interest Degree and the influence of fuzziness of
evaluation objects on Interest Degree.
