TY - GEN
T1 - Visualization of relations of stores by using Association Rule mining
AU - Yamada, Sanetoshi
AU - Funayama, Takamitsu
AU - Yamamoto, Yoshiro
N1 - Publisher Copyright:
© 2015 IEEE.
PY - 2015/12/29
Y1 - 2015/12/29
N2 - 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.
AB - 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.
KW - Association Rule Analysis with User Attributes
KW - Conditional Association Rule Analysis
KW - Visualization of Association Rules
UR - http://www.scopus.com/inward/record.url?scp=84960328377&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=84960328377&partnerID=8YFLogxK
U2 - 10.1109/ICTKE.2015.7368463
DO - 10.1109/ICTKE.2015.7368463
M3 - Conference contribution
AN - SCOPUS:84960328377
T3 - International Conference on ICT and Knowledge Engineering
SP - 11
EP - 14
BT - Proceedings - 2015 13th International Conference on ICT and Knowledge Engineering, ICT and KE 2015
PB - IEEE Computer Society
T2 - 13th International Conference on ICT and Knowledge Engineering, ICT and KE 2015
Y2 - 18 November 2015 through 20 November 2015
ER -