# A Review of Data Mining with Big Data towards Its Applications in the Electronics Industry
## 6 Conclusions and Future Research Directions
DM has been widely used in the stage of design and production especially for wafer fab and PCB assembly, and the pervasive knowledge areas include QI, PMO, AEC/APC, and so forth. However, potential applications such as customization production, procurement, warehouse management and inventory balance, and equipment maintenance and repair require more relevant data accumulation and extended mining. The global logistics industry has a large ever-growing amount of Big Data and is flooded with real-time data ranging from smartphones, sensors, and digital machines [9]. However, the application of DM with Big Data in SCM and logistics for electronic products has attracted few special discussions. Meanwhile, little effort has been put on CRM and order management combining the features of electronics such as a large amount of consumers, fast replacement of new products, and fierce market competition. The patterns and knowledge hidden in Big Data are multidimensional (for example, various departments and lifecycle stages) and scattered, which hinders the effective mining and utilization of the knowledge. Therefore, further studies can be conducted to mine consumer habits and market characteristics to support more reasonable decision for customization product development, market pricing, and maintenance based on the association, prediction, and time series analysis functions. The fast upgrading of electronic products resulting in a large number of e-waste and the use of DM and Big Data to improve the efficiency and effectiveness of its energy saving, recycling, reverse logistics, and reduction of environmental risks are a worthwhile attempt. More importantly, the macro strategy for integrated mining and integration applications for the whole lifecycle should be considered and developed by enterprises. (3) DM functions and DMTs. The prediction, classification, and clustering are the most frequently used DM functions while the other three functions (outlier detection, association and time series analysis) have been used only in a few situations. The extended investigation of outlier detection, sequential pattern mining, and time series analysis considering time information for online model development and updating could enable companies to respond promptly to dynamic and emerging situations. For DMTs, the parameter optimization of DMTs, such as ANN and SVM, requires continuous study. While FCM and fuzzy logic have been combined with ANN to handle uncertainty, they might be combined with other related mechanisms such as SVM and regression. Additionally, these approaches would handle Big Data with easy implementation and high performance, and more deliberate consideration for industrial applications is required. (4) Algorithm performance. In general, it is difficult to obtain results with obviously competitive advantage in the existing single algorithm. Generally, a hybrid mining algorithm needs to be constructed based on the characteristics of the problem by integrating different functions and different DMTs so as to ensure the validity and advantage of the algorithm. How to set and optimize algorithm parameters, such as parameters of ANN and SVM, also remains to be further studied. Meanwhile, how to evaluate the advantages and disadvantages of the developed algorithm dynamically and ensure the robustness of the algorithm under certain data loss and redundancy needs to be further compared. How to evaluate the under-fitting and overfitting of algorithms and balance of the two has been paid less attention and requires further consideration. (5) Software and implementation: Many researchers employed special purpose tools, such as NeuroSolutions, Neural Network Toolbox, LibSVM, Fuzzy Logic Toolbox, and SOM toolbox to implement the developed algorithms. Meanwhile, many approaches were developed by Matlab. A dedicated software package and Matlab integration of the basic engine allowed researchers to implement the proposed algorithm and verify the results more easily.