# Data Mining and Augmented Reality: An Application to the Fashion Industry
## 4 Results
### Association Rule Mining
#### 4.1.2 Defect Identification  
As described before, ARM can be used to determine the most likely locations for defects during the quality control phase in order to guide the operator through the inspection. In this case, the dataset used for ARM refers only to defective articles. For example, in the proposed application, rules relating to product category, sample size, article material, supplier code, control area to perform the control set and the defect description have been taken into account. The idea beneath this assumption is to rank the possible locations where a defect could be located based on the historical performance of the supplier on the specific product. The association rules are mined every two weeks using the data updated to find new relationships, allowing a relevant number of new outcomes to be obtained and, thus, producing potentially new relationships. \begin{table}
\begin{tabular}{l l l l} \hline \hline \(\mathbf{X}\) & \(\mathbf{Y}\) & \(\mathbf{Supp}\) & \(\mathbf{Conf}\) \\ \hline Supplier code = 7, Sample size = 15\%, & Outcome = & & \\ Product category = Bags, & Non-compliant & \textless{}0.001 & 0.143 \\ Article material = 118 & Non-compliant & \textless{}0.001 & 0.143 \\ Supplier code = 7, Sample size = 15\%, & & \\ Product category = Bags & Outcome = Compliant & \textless{}0.001 & 0.857 \\ Article material = 118 & & \\ Supplier code = 7, Sample size = 15\%, & Outcome = & \textless{}0.001 & 0.250 \\ Article material = 475 & Non-compliant & \textless{}0.001 & 0.750 \\ Supplier code = 7, Sample size = 15\%, & & \\ Product category = Bags & Outcome = Compliant & \textless{}0.001 & 0.750 \\ Article material = 475 & & \\ Supplier code = 7, Sample size = 15\%, & & \\ Article material = 661 & & \\ Supplier code = 7, Sample size = 15\%, & & \\ Product category = Bags & Outcome = Compliant & \textless{}0.001 & 0.545 \\ Article material = 661 & & \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Association rules relating to supplier code, sample size, product category, article material, and outcome. As an example, let us consider the case of supplier 7, which is controlled with a sample size equal to 15% when article material 118 is delivered (Table 4). The quality controller, according to the rules, will be informed that the first control area to check will be the body, followed by the handles. Indeed, the former has a probability of hosting a defect equal to 60% (conf = 0.600), while the latter of 40% (conf = 0.400). The following step requires deepening the analysis indicating the control set of activities that should be completed:  
Table 5 shows that both cases indicate a single action to take to respond to the identified defect, i.e., Control set = Internal (conf = 100%) and Control set = External (conf = 100%). Usually, the defect is detected by inspecting the internal part of the product and its lining for the body while limiting it to the external part for the handle. More in detail, Table 6 shows the precise type of defects that one should expect during the inspection of the external part of the bag so that the operator has all the information available to support their decision. As soon as the sample has been completely evaluated, the operator will be allowed to define the outcome, and the dataset will be automatically updated. In this way, the inspection is sped up in cases of the product's non-compliance since it is sent back to the producer. In addition, this represents valid support for non-expert operators which are guided through the process.