# A Review of Data Mining with Big Data towards Its Applications in the Electronics Industry
## 6 Conclusions and Future Research Directions  
This paper presents a comprehensive review of DM with Big Data towards its applications in the electronics industry. We can see that the DM with Big Data has been applied to different scenes including product design improvement, manufacturing process optimization, PMO, production process monitoring and control, quality improvement, CRM, and so forth. Customer-oriented product development and process plan optimization are the main applications for product design improvement and manufacturing process optimization in the stage of design. Prediction was the most frequently used DM function observed in the reviewed articles. ANN and regression were the widely used DMTs for the prediction. The application of DM with Big Data for process monitoring and control, PMO, and quality improvement in the stage of production has attracted the interest of most research. On the one hand, sophisticated DM and Big Data related techniques such as FDC and R2R have been developed for the wafer production process monitoring and control to reduce defects and improve the quality/yield based on the data collected from manufacturing processes, equipment/tool/environment statuses, and process parameters. The functions of classification and clustering were widely used for FDC based on related DMTs such as DT, SVM and ANN, k-means and SOM, while the prediction function was widely presented for VM based on ANN, regression, and SVM. On the other hand, prediction, clustering, and the combination of the two are the most frequently employed functions for the optimizing scheduling plan and prediction of cycle time/due date based on ANN, FCM, SOM, and a hybrid of fuzzy logic and ANN. Additionally, post hoc diagnosis, quality prediction, and classification were conducted based on the functions of prediction, classification, clustering, and association for future production quality improvement. Most of the DM applications are related to CRM at the stage of SSR for the purpose of acquiring and retaining potential customers and maximizing customer value based on the records of transaction and online feedback from customers. Prediction, classification, clustering, and time series analysis functions were conducted based on ANN, regression, and SVM for sale and service to mine the consumption habits and predict the marketing price. The achievement of the reviewed articles facilitates theoretical study and practical application of DM with Big Data to the electronics industry. Nevertheless, the limitation and challenges still exist for future research. (1) Data preparation and preprocessing. The data of the product lifecycle are characterized by multisource (for example, design, production, and service data), heterogeneity (for example, structured, semi-structured, and unstructured data), and \"noise\" (for example, incomplete, incorrect, redundant, and inconsistent data) [1]. These problems increase the difficulties of data preparation, preprocessing, and subsequent mining, and also generate misleading patterns. However, little effort has been devoted to handling these problems. Manufacturing organizations with well-established and integrated data collection systems would benefit from a larger application of DM and Big Data [4]. Unified management and storage of the multi-source and heterogeneous data are necessary, and this motivates enterprises to develop DM strategies with dedicated consideration to data accumulation, integration, and consistency. Multi-business requirements integration, concept standardization, unified model establishment, and data/system interface development should be conducted collaboratively to facilitate data utilization. The standardization of operations such as data entry, storage, and maintenance should also be conducted accordingly to ensure the data quality and reduce data redundancy. (2) The knowledge area of DM application.