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 |