TY - GEN
T1 - Mining Association Rules in Seasonal Transaction Data
AU - Ayu, Sabrina Kusuma
AU - Surjandari, Isti
AU - Zulkarnain, Zulkarnain
N1 - Publisher Copyright:
© 2018 IEEE.
PY - 2018/7/2
Y1 - 2018/7/2
N2 - Nowadays global retail business faces new challenges due to changes in consumer buying preferences, likewise in Indonesia that consequence increasing level of competition among retailers. Moreover, Indonesia's government announced major reforms in foreign investment in 2016 which attracted international retailers. Therefore, local retailers have to plan a strategy to expand the current market through enhancement of customer satisfaction related to their purchasing activities. This research aims to analyze market basket data to help local retailers understand consumer purchase behavior by finding association pattern. Nevertheless, the retail industry is highly seasonal. Hence, there are three types of season in retail industry which were proposed as classification in this research, i.e. peak season, normal season, and slack season. Based on the result, each season has similarity in generated pattern among other seasons. However, there are also 6 unique patterns found from a certain season. By knowing the discovered association patterns in each season, the company may determine product offering or promotion strategy for different season.
AB - Nowadays global retail business faces new challenges due to changes in consumer buying preferences, likewise in Indonesia that consequence increasing level of competition among retailers. Moreover, Indonesia's government announced major reforms in foreign investment in 2016 which attracted international retailers. Therefore, local retailers have to plan a strategy to expand the current market through enhancement of customer satisfaction related to their purchasing activities. This research aims to analyze market basket data to help local retailers understand consumer purchase behavior by finding association pattern. Nevertheless, the retail industry is highly seasonal. Hence, there are three types of season in retail industry which were proposed as classification in this research, i.e. peak season, normal season, and slack season. Based on the result, each season has similarity in generated pattern among other seasons. However, there are also 6 unique patterns found from a certain season. By knowing the discovered association patterns in each season, the company may determine product offering or promotion strategy for different season.
KW - Apriori algorithm
KW - data mining
KW - market basket analysis
UR - http://www.scopus.com/inward/record.url?scp=85062080149&partnerID=8YFLogxK
U2 - 10.1109/ICISCE.2018.00074
DO - 10.1109/ICISCE.2018.00074
M3 - Conference contribution
AN - SCOPUS:85062080149
T3 - Proceedings - 2018 5th International Conference on Information Science and Control Engineering, ICISCE 2018
SP - 321
EP - 325
BT - Proceedings - 2018 5th International Conference on Information Science and Control Engineering, ICISCE 2018
A2 - Li, Shaozi
A2 - Dai, Ying
A2 - Cheng, Yun
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 5th International Conference on Information Science and Control Engineering, ICISCE 2018
Y2 - 20 July 2018 through 22 July 2018
ER -