## 4 Results
### Validation for Product Recommendation System  
The recommendation system based on association rules was validated and applied using a classifier algorithm. Four different algorithms, including a random forest classifier, a combination of decision tree with bagging algorithm, a combination of decision tree with AdaBoost algorithm, and a K-nearest neighbor, were trained and evaluated in an experiment. Table 13 presents the performance of these algorithms. The combination of the decision tree with the AdaBoost algorithm generated the most accurate result based on the training model, with results on accuracy (0.096), precision (0.43), recall (0.13), and F1-scores (0.12). On the other hand, there were also good results for the testing model (accuracy = 0.051, precision = 0.11, recall = 0.09, F1-score = 0.09). By comparing to the other, \begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Cluster** & **Antecedents** & **Consequents** & **Support** & **Confidence** & **Lift** \\ \hline Potential & Toys & Stationery, Storage & 20.047\% & 65.421\% & 1.672 \\ Potential & Stationery, Storage & Toys & 20.047\% & 51.228\% & 1.672 \\ Potential & Bag, Gift & Stationery, Decoration, Kitchen & 21.443\% & 78.506\% & 1.630 \\ Potential & Stationery, Decoration, Kitchen & Bag, Gift & 21.443\% & 44.520\% & 1.630 \\ Potential & Itemsis & Stationery, Kitchen Utensils & Stationery, Kitchen Utensils & 21.443\% & 81.409\% & 1.626 \\ Potential & Stationery, Kitchen Utensils, Gift & Bag, Decoration, Gift & 21.443\% & 42.835\% & 1.626 \\ Potential & Decoration, Kitchen Utensils, Gift & Stationery, Bag & 21.443\% & 55.465\% & 1.619 \\ Potential & Stationery, Bag & Deoration, Kitchen Utensils & 21.443\% & 62.606\% & 1.619 \\ Potential & Decoration, Bag, Kitchen Utensils & Stationery, Gift & 21.443\% & 51.945\% & 1.614 \\ Potential & Stationery, Gift & Decoration, Bag, Kitchen & 21.443\% & 66.626\% & 1.614 \\ \hline Loyal & Stationery, Kitchen Utensils & Bag, Storage & 20.43\% & 47.11\% & 1.599 \\ Loyal & Bag, Storage & Stationery, Kitchen Utensils & 20.43\% & 69.35\% & 1.599 \\ Loyal & Bag, Kitchen Utensils & Stationery, Storage & 20.43\% & 50.82\% & 1.593 \\ Loyal & Stationery, Storage & Bag, Kitchen Utensils & 20.43\% & 64.03\% & 1.593 \\ Loyal & Stationery, Decoration & Bag, Storage & 20.41\% & 46.38\% & 1.575 \\ Loyal & Bag, Storage & Stationery, Decoration & 20.41\% & 69.30\% & 1.575 \\ Loyal & Stationery, Storage & Bag, Decoration & 20.41\% & 63.98\% & 1.562 \\ Loyal & Bag, Decoration & Stationery, Storage & 20.41\% & 49.83\% & 1.562 \\ Loyal & Stationery, Bag & Kitchen Utensils, Storage & 20.43\% & 71.24\% & 1.550 \\ Loyal & Kitchen Utensils, Storage & Stationery, Bag & 20.43\% & 44.44\% & 1.550 \\ \hline \hline \end{tabular}
\end{table} 
Table 11: Top Ten Association Rule for Customer-Oriented Group (each cluster). \begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Cluster** & **Antecedents** & **Consequents** & **Support** & **Confidence** \\ \hline Best-Seller & Loyal & Potential & 91.62\% & 93.22\% \\ Best-Seller & Potential & Loyal & 91.62\% & 98.88\% \\ \hline Profitable & Potential & Loyal & 88.52\% & 100\% \\ Profitable & Loyal & Potential & 88.52\% & 88.52\% \\ \hline VIP & Potential & Loyal & 88.52\% & 100\% \\ VIP & Loyal & Potential & 88.52\% & 88.52\% \\ \hline \hline \end{tabular}
\end{table} 
Table 12: Association Rule for Product-Oriented Group (each cluster). both models shoed a big deviation, meaning that while the training model can predict accurately, the algorithm cannot classify correctly on the testing model, which symbolizes an overfitting problem. This also occurred with the random forest classifier, which on the training model had similar results as the decision tree and AdaBoost algorithm (accuracy = 0.094, precision = 0.44, recall = 0.1, F1-score = 0.12). Meanwhile, the testing model proved to be an overfit model (accuracy = 0.042, precision = 0.1, recall = 0.06, F1-score = 0.07).