## 4 Results
### Association Rules Mining  
The results in Table 11 show the ten best association rules for the customer-oriented dataset. Each rule corresponds to a specific cluster. Several patterns of co-occurring items appear in customers' transactions, indicating possible relationships between products frequently purchased together. The \"potential\" cluster shows the strongest associations between \"toys\", \"stationery\", \"storage\", \"bag\", \"gift\", \"decoration\", and \"kitchen utensils\". Customers in this cluster are 65.421% confident they will buy \"toys\" when they also purchase \"stationery\" and \"storage.\" Similarly, they express 78.506% confidence in buying a combination of \"bag\", \"gift\", \"stationery\", \"decoration\", and \"kitchen utensils\". These strong association rules suggest that offering or promoting these product combinations as bundles or in proximity may encourage more sales and enhance customer satisfaction. However, the \"loyal\" cluster contains association rules involving \"stationery\", \"kitchen utensils\", \"storage\", \"bag\", and \"decoration\". Customers in this cluster are 69.35% confident in purchasing \"stationery\" and \"kitchen utensils\" when they also purchase \"bags\" and \"storage\". Their confidence in buying a combination of \"stationery\", \"bags\", \"kitchen utensils\", and \"storage\" is also 70.24 percent. Based on these findings, it is likely that customers in this cluster prefer specific combinations of products. Offering these combinations as tailored packages or promotions may increase brand loyalty and repeat purchases.  
Table 12 displays the association rules for the product-oriented group, describing the likelihood of each customer group purchasing the same product category due to the influence of another customer group. For instance, in the best-seller product category, the potential-loyal relationship has a confidence level of 98.88%, higher than the loyal-potential relationship (93.22%). This finding indicates that potential customers have a higher chance of influencing loyal customers to purchase the same product category. Otherwise, in another two clusters, both agree that potential-loyal associations are more confident (100%) than the association of loyal-potential (88.52%). These results can be used as a strategy to generate additional income and increase customer engagement caused by the association effect of each customer in terms of purchasing and promoting any prod  
\begin{table}
\begin{tabular}{c c c c c c c c c c} \hline \hline  & \multicolumn{2}{c}{**Cluster 1**} & \multicolumn{2}{c}{**Cluster 2**} & \multicolumn{2}{c}{**Cluster 3**} \\  & \multicolumn{2}{c}{**(Best Seller\(-\)4870 Products)**} & \multicolumn{2}{c}{**(Profitable\(-\)391 Products)**} & \multicolumn{2}{c}{**(VIP\(-\)18 Products)**} \\ \hline  & Frequency & Monetary & Periodicity & Frequency & Monetary & Periodicity & Frequency & Monetary & Periodicity \\ Min & 1.000 & 0.017 & 0.000 & 3.000 & 0.480 & 1.239 & 3.000 & 0.570 & 0.425 \\ Mean & 155.932 & 454.953 & 10.050 & 15.163 & 58.806 & 91.096 & 900.722 & 14,780.086 & 249.059 \\ Max & 2045.000 & 12,528.000 & 51.309 & 2099.00 & 10,013.57 & 231.316 & 5016.000 & 146,269.190 & 498.507 \\ \hline \hline \end{tabular}
\end{table}
Table 10: Product Segmentation Interpretation.  
uct. For example, the company could offer coupons or additional discounts to potential customers to purchase best-seller products, thereby increasing the likelihood of loyal customers also purchasing in the same category. Additionally, in this situation, the effects of word-of-mouth marketing carried out by each antecedent to the consequents may play a crucial role. For instance, in the case of profitable and VIP products, it is more effective if the company prioritizes marketing the product to potential customers first so that they can automatically suggest the same product to loyal customers.