# A Review of Data Mining with Big Data towards Its Applications in the Electronics Industry
## 4 Data Mining with Big Data Applications in the Electronics Industry
### Software Used for the Selected Articles  
Many algorithm engines, tools, and platforms have been developed to implement functions and related DMTs. Predictive analytics today summarized the top 50 free DM software [131], including Orange, RapidMiner, Weka, KNIME, SpagoBI, Anaconda, Octave, and so forth. Some commercial software including Sisense, Oracle Data Mining, Microsoft SharePoint, IBM Cognos, Dundas BI, SAP Business Objects, Matlab, Statistic, SAS EM, SPSS Clementine (IBM SPSS Modeler after 2009), Tanagra, Qlik Sense, and so forth have also been widely used by researchers and practitioners. The different tools shown in Table 14 have been used for various purposes in mining applications from the reviewed literature. The category of software in the reviewed articles can be categorized into spreadsheets, statistical software package, DM software package, general purpose software, special purpose tools, and high-level languages. Some statistical software packages such as MiniTab, SAS, SPSS, and Statistics were preferred for implementing SA-oriented methods such as MRA and ANOVA. Spreadsheet-application excel was mainly used for data preparation and preprocessing. However, commercial software packages such as SPSS Modeler, SAS Enterprise Miner (SAS EM), were only used in a few of the applications. The general purpose software Matlab and special purpose packages based on Matlab were used in various applications for the design and production of QI, PMO, and CRM. They were mostly utilized to realize ANN, fuzzy logic, SVM, and SOM supported by several open source toolboxes such as NeuroSolutions, Neural Network, NeuralPower, Fuzzy Logic, LibSVM, and SOM. The association, outlier detection, and time series analysis functions were mainly conducted by commercial software packages such as SAS EM [38], and RapidMiner [42]. Some high-level languages such as C/C++ [94,120] and Visual Basic [70, 71, 72, 73, 75, 76, 77] were used for SOM, fuzzy c-means, fuzzy logic, ANN, and the combination of these approaches for its flexibility for researcher to design or combine particular methodologies considering domain knowledge in handling and analyzing the data. Meanwhile, some platforms such as the online system [79], fab-wide FDC [80], VM system [83], online time series prediction system [88], and wafer bin of map clustering and classification systems [117] have been developed for different tasks of AEC/APC based on high-level languages. However, the commonly used platforms for developing DM or Big Data application system such as WEKA [28], RapidMiner [42], R software environment [122], and Python [84] have  
Figure 8: The different DMTs application in different knowledge areas. been utilized by only a few of the researchers, indicating that the systematized applications of these results still require further development by practitioners. The product lifecycle processes carry a huge number of structured, semi-structured, and unstructured data. Big Data analytics and DM technology can be used to make a deep analysis of historical lifecycle data, to discover knowledge, and to optimize the process of PLM. A framework with four modules including data sensing and acquisition, data processing and storage, DM model development, and Big Data application in PLM was presented by [PERSON] et al. [1]. However, the summarization and classification of lifecycle related data and its utilization by different knowledge areas have not been discussed. Meanwhile, the special application scheme for electronics manufacturing has not been considered. Therefore, the establishment of a diagram of data content for different knowledge areas and DM with Big Data framework for the electronics industry can guide companies to accumulate related data and develop DM strategy from the view of lifecycle and overall business chain, which can also facilitate researchers and practitioners to select appropriate techniques and better utilization of data for knowledge discovery.