# Data Mining and Augmented Reality: An Application to the Fashion Industry
## 2 Materials and Methods  
### Research Approach  
This paper proposes an approach aiming at improving the quality control process in a 2-fold manner: from a strategic point of view, by adapting the sample size on the bases of suppliers' performance, aiming to encourage virtuous behaviors from them in order to be able to reduce sample sizes and gain competitive advantage; from an operational point of view, by guiding the quality control inspection towards the more likely occurrences depending on the specific product, capitalizing on the visualization of helpful inputs through AR devices, in order to hasten the inspection process. Production and quality data represent the basis of the proposed approach and have to be frequently updated in order to obtain up-to-date analysis and information. Information on previous control outcomes, sample size, and policies in case of non-compliance is needed for all the suppliers and for all the products and has to be collected or retrieved by existing information systems. Once a solid and reliable dataset is available, the analytics can be performed by mining the association rules. The focus of the proposed approach (i.e., Figure 1) is centered on identifying the compliance rate of a supplier considering the associated sample size and, possibly, detailing this value per article material and product category. The association rules will be used to re-arrange the quality control process, e.g., the sample size, on the basis of suppliers' performance and periodically verify whether such a size is still suitable. Figure 1: Framework of the proposed research approach. The proposed procedure is carried out incrementally, including different attributes in the analysis, as follows:  
1. Relating a supplier and the associated sample size to the outcome of the quality inspections: in this way, the ability to provide compliant lots is verified at a general level. In particular, if the supplier is able to provide an acceptance rate of 100% in all cases, then there is no need to analyze furtherly the performance in order to identify virtuous and vicious products. On the other end, it should be discussed whether a lower sample size could be selected to hasten the quality control phase;
2. Relating a supplier, the associated sample size per product category to the outcome of the quality inspections: this group of rules is focused on identifying the impact of the product category on the performance of the supplier: this should be the case for suppliers producing a variety of product categories, but having specific expertise only on manufacturing a selection of them. If the supplier provides an acceptance rate of 100% for a specific product category, then the same remarks made at point 1 can be repeated, i.e., the sample size can be reduced, and the analysis should not be reiterated;
3. Relating a supplier, the associated sample size per product category and article material to the outcome of the quality inspections: indeed, recurrent non-compliance can regard the characteristics of the main material composing the article. Being able to quantify such impact represents a valuable input. As a second objective, rules can be mined to determine, depending on specific characteristics, which are the most likely locations where defects could be detected, so that the operator is directly guided towards them, receiving relevant information like the control set that should be carried out or the aspect of the defect, and the quality control process can be expedited. The roadmap for mining the association rules can be structured as follows:  
1. Firstly, the most common areas of the product in which defects are frequently localized should be identified so that the operator focuses on the most critical at first. That said, the occurrences are ranked by confidence, showing at the top of the list the most common ones (i.e., the ones having the highest probability);
2.