For questionnaire data, a method is needed to understand questionnaire results and to find characteristics of questionnaire results by gender and generation. We previously suggested visualization of Association Rules to extract the characteristics of attributes (Yamada and Yamamoto, 2014). In this study, we find the relations between item classifications by using visualization of Association Rules for purchasing data. But, when we perform an Association Rule analysis for a large quantity of data, it is difficult to find meaningful rules because the support generally falls. When we extract rules of lower support, too many rules are extracted. Therefore, we propose a Conditional Association Rule Analysis and an Association Rule Analysis with User Attributes. In this study, we improve the visualization of Association Rules by Conditional Association Rule Analysis and the Association Rule Analysis with User Attributes.