Analysis of Customer Product Interests using the Market Basket Analysis Model with Hash-Based Algorithm and Association Rules

The Market Basket Analysis method can be used for analyze pattern shopping consumer. With take advantage of later data processed for get information from transaction dataset that. Supermarkets are business businesses engaged in the sale needs tree. Shop this not yet knowing pattern shopping on cart shopping consumer. Algorithm used that is hash-based algorithm because algorithm this reduce amount candidate itemset at the start of. Research results this namely found 2 Frequent itemset namely bread and butter with support 96 ago eggs, bread and butter with support 97. Based on results in implementation hash-based algorithm and association rules, we can conclude that the manager of the supermarket can start use same way for knowing customer interest in something product with product other. With this manager can more focus with product the for reduce loss caused products that are not behavior.

[1]  Mohammad A. Alomari.  The Legal System for the Conversion of Commercial Companies in the Light of the Rules of the Saudi Corporate System , 2022, International Journal for Applied Information Management.

[2]  Chonghuan Xu,et al.  An Improved Evaluation Methodology for Mining Association Rules , 2021, Axioms.

[3]  Kentaro Takakuwa.  Difficulties of Integrating Human Resources Management Globally by Japanese Corporations , 2021 .

[4]  C. Nguyen,et al.  Analysis Of Internal Factors Affecting Bank Probability: Evidence From Listed Banks On Vietnam Stock Market , 2021 .

[5]  Chi Thi Dieu Nguyen.  Analysis Of Internal Factors Affecting Bank Probability: Evidence From Listed Banks On Vietnam Stock Market , 2021, IJIIS: International Journal of Informatics and Information Systems.

[6]  Mohamed Cheriet,et al.  Core and Spectrum Allocation Based on Association Rules Mining in Spectrally and Spatially Elastic Optical Networks , 2021, IEEE Transactions on Communications.

[7]  John Yeung,et al.  Data Analytics Architectures for E-Commerce Platforms in Cloud , 2021, International Journal for Applied Information Management.

[8]  Bambang Pujiarto,et al.  With topological data analysis, predicting stock market crashes , 2021 .

[9]  R. Riyanto,et al.  Modelling Customers Lifetime Value For Non-Contractual Business , 2021, IJIIS: International Journal of Informatics and Information Systems.

[10]  Alain Yee-Loong Chong,et al.  Predicting customer demand for remanufactured products: A data-mining approach , 2020, Eur. J. Oper. Res..

[11]  Hyosik Yang,et al.  An intelligent system based on optimized ANFIS and association rules for power transformer fault diagnosis. , 2020, ISA transactions.

[12]  Qian Wang,et al.  Data mining of customer choice behavior in internet of things within relationship network , 2020, Int. J. Inf. Manag..

[13]  Yüksel Akay Ünvan.  Market basket analysis with association rules , 2020 .

[14]  Yi-Mu Chen.  The Charisma of Online Group-Buying: The Moderating Role of Social Motivation , 2019, IJIIS: International Journal of Informatics and Information Systems.

[15]  Yo Ceng Giap,et al.  Study of application of data mining market basket analysis for knowing sales pattern (association of items) at the O! Fish restaurant using apriori algorithm , 2019, Journal of Physics: Conference Series.

[16]  Baofeng Huo,et al.  Bridging customer knowledge to innovative product development: a data mining approach , 2019, Int. J. Prod. Res..

[17]  V. Tran.  Exploring The Relationships Among Social Benefits, Online Social Networks Dependency, Satisfaction and Youth's Habit Formation , 2018, IJIIS: International Journal of Informatics and Information Systems.

[18]  Fachrul Kurniawan,et al.  Market Basket Analysis to Identify Customer Behaviours by Way of Transaction Data , 2017, Knowl. Eng. Data Sci..

[19]  Tran Van Dat,et al.  EXPLORING THE RELATIONSHIPS AMONG SOCIAL BENEFITS, ONLINE SOCIAL NETWORK DEPENDENCY, SATISFACTION, AND YOUTH’S HABIT FORMATION , 2015 .

[20]  Athanasios V. Vasilakos,et al.  Association Rules Mining Among Interests and Applications for Users on Social Networks , 2019, IEEE Access.