# Data Mining and Augmented Reality: An Application to the Fashion Industry
## 1 Introduction
In this context, Association Rule Mining (ARM) can prove valid support in defining hidden relationships in conditions occurring together [11]; several applications can be found in the literature in highly automated production processes (e.g., refining processes [12], automotive, aeronautics and pharmaceutics [13]), while limited contributions can be found in low-tech sectors, like the fashion industry. For example, [PERSON] et al. [14] used ARM to define common fabric parameters leading to defective or high-quality outputs. Similarly, [PERSON] et al. [15] related production process parameters to garments and then based an improvement plan for production quality on association rules' results [16]. Besides the increasing need for analyzing huge amounts of unstructured data, companies are demanding to clearly visualize the resulting information [17]. The need to present data is increasing, especially in manufacturing environments, due to the large application of Industry 4.0 technologies that allow us to regularly acquire raw data [18]. Data interpretation and visualization, indeed, are becoming a challenge for decision-making because only a clear understanding may result in making informed decisions [17]. Augmented Reality (AR) allows us to intuitively explore data overlaying digital content in a real environment [19], with virtual things that coexist with real ones in the same space [20]. As AR is becoming more affordable and mature, it can be used as a tool to visualize data in context to support making more informed decisions [21]. This technology is implemented on wearable devices or even smartphones and tablets, with several applications as decision support systems that can be found in the literature. [PERSON] et al. use augmented and mixed reality to visualize complex data related to resource plans [17]. In the work of [PERSON] results and analytics are displayed through AR to increase the understanding of complex models [22]. A decision support framework based on semantic AR has been developed by [PERSON] et al. to reduce information overloading and be effective in IoT data interpretation [23]. [PERSON] et al. propose a mobile assistance system based on AR for order picking [24]. In the field of quality management, [PERSON] et al. developed an application that integrates Building Information Modeling (BIM) and AR to acquire inspection data and show processed results [25]. [PERSON] et al. developed a mobile application based on AR and Finite Element Method (FEM) to prevent unexpected failure conditions [26]. [PERSON] et al. provided a predictive maintenance system that includes Industry 4.0 technologies and principles for the earlier detection of machine failures and to support technicians during interventions [20]. The relevance of AR in shortening training time in a variety of fields is also demonstrated by the review by [PERSON] et al. [27]. They even highlight the main benefits that could be gained through this technology, including improved spatial cognition, assembly skills and reduction of operational errors. The advantages of supporting decision systems with AR are also explored by [PERSON] et al., who identified the ability to captivate decision makers' awareness and facilitate flaw detection as well as high work productivity [21]. To sum up, even if data mining and AR have been widely used to support decision-makers, there is a lack of joint contributions in the quality field. Moreover, a few solutions based on those technologies are being explored within the fashion industry. A data-driven approach based on data mining and AR has therefore been proposed to support companies in data interpretation and visualization within the quality management field. More in detail, the framework allows users to make informed decisions in setting sampling rules based on suppliers' quality performance per product. It will avoid the quite common definition of general sample sizes that could require extra time for controlling items historically characterized by high levels of compliance.