# Data Mining and Augmented Reality: An Application to the Fashion Industry
## 1 Introduction  
Nowadays, a wider implementation of Industry 4.0 (I4.0) tools by several companies are increasing the collection of huge amounts of data by companies in different fields, such as production and quality management. To face the challenge of reducing lead time to meet the market, companies are even more involved in managing the trade-off between guaranteeing the outstanding quality level final consumers require and speeding up the fault detection time. In this scenario, the identification of the most appropriate dimension of the control lot impacts, on the one hand, on the interception of product defects, as the more items are checked, the more reliable the control outcome. On the other hand, the fewer items are controlled, the shorter the time for the quality process. To decrease the time to detect product non-compliances, controllers need to be supported in checking the first the most current defects. This way, the sooner they are intercepted, the sooner the product outcome is defined as non-compliant, with no need to proceed with further time-consuming controls. Considering trainees instead of experts, controls are also sloweddown by the time needed to clearly understand the defects to be checked. The trade-off between increasing quality controls to intercept product defects and decreasing the time for checking represents, therefore, a critical challenge, especially for dynamic contexts where the most occurrent defects change rapidly over time and consumers require outstanding quality, like the fashion industry. The huge amount of data collected even by low-tech companies through I4.0 solutions to trace quality control outcomes remains quite often non-used, leading to the \"rich data but poor information\" issue [1]. The elaboration of data regularly extracted over time requires automation, especially in dynamic and low-tech contexts, discouraging the use of statistics made only by skilled resources. Easy-to-update datasets are needed to extrapolate knowledge and support controllers in making decisions based on up-to-date information. Data mining techniques are often used in the field of knowledge acquisition from unstructured data, especially when hidden relationships need to be discovered to support decision-makers [2]. For instance, to shorten the duration of the quality control process, historical data analysis allows us to improve inspection policies. Currently, the definition of quality controls is mainly led by international standards, such as the UNI ISO 2859-1:2007, for the identification of the acceptable quality level (AQL) for lots received from suppliers to be controlled [3]. This approach leads to the use of static parameters, which are not well-suitable for fast-changing scenarios. In order to achieve the required AQL, researchers suggest that quality management should also be evaluated relating to economic aspects, production management and maintenance management, basing the sample size on a dynamic definition [4]. [PERSON] et al. [5] considered the latter aspect taking into account failure rates, production efficiency and machinery remaining useful life, while [PERSON] [6] introduced hypothesis testing to identify the best sampling strategy considering the perspective of consumers and producers. Low-tech and dynamic sectors, which are characterized by manual processes and operations, are less likely to implement previously mentioned techniques [7], even though automated and data-driven analysis through data mining approaches could result in benefits, as testified by existing contributions [8]. In [PERSON] and [PERSON] [9], for example, the identification of attributes majorly influencing product quality in the low-tech environment but characterized by a large amount of data is explored. These considerations bring us to consider data mining as a possible facilitator in predicting product or process quality when the final objective is obtaining useful knowledge from data [10].