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Table 1 Comparison of the most important the advantages and disadvantages of the reviewed previous methods to an analytics model for customers’ basket

From: An analytics model for TelecoVAS customers’ basket clustering using ensemble learning approach

Authors Refs Method Advantages Disadvantages
Seyedan and Mafakheri [16] Presented a classification of these algorithms and their applications 1. Good accuracy 1. Complex model
Yudhistyra and Risal [17] New method for implementing big data in this version combines the CRISP-DM framework and key steps for customer analysis. 1. High speed 1. Low accuracy
2. Low precision
Jiang et al. [18] New methodology for dynamic modelling of customer preferences based on online customer 1. Good accuracy 1. Low speed
Musalem et al. [9] The methodology also yields a segmentation of shopping trips based on the composition of each shopping basket 1. High speed 1. Low accuracy
2. Low precision
Szymkowiak et al. [10] An Apriori algorithm for customer basket analysis 1. Good accuracy 1. Complex model
Jain et al. [12] A statistical method to make the service closer to the person concerned 1. Normal accuracy 1. Complex model
Srivastava et al. [13] Used a portfolio optimization model of customer basket 1. High speed 1. Low accuracy
2. Low precision
Kurniawan et al. [8] Associative and data mining techniques such as neural networks and Apriori. 1. Fast Execution time 1. High MAE
2. High RMSE
Kaur and Kang [5] A customer basket analysis model using a combination of data mining methods and association rules 1. High speed 1. Low accuracy
2. Low precision
Venkatachari et al. [19] Used a combination of associative approaches such as Apriori and FP-Growth to analyze customer baskets 1. High speed 1. Low accuracy
2. Low precision
Sherly and Nedunchezhian [20] A parallel and distributed techniques and associative rules to analyze the customer basket 1. Normal accuracy 1. Low speed
Deng et al. [14] A process-based approach and algorithm for extracting iterative patterns such as N-List 1. Good accuracy 1. Complex model
Abdiansah and Wardoyo [15] Increases the accuracy of customer basket extraction and analysis. With the help of deep neural network algorithms, C4.5 decision tree and SVM-Lib algorithm 1. Good accuracy 1. Complex model