Data mining is a crucial step in forming new knowledge or so-called Knowledge Discovery in Databases. To be more exact, this process is based on extracting useful information from databases. However, to complete data mining, it is necessary to transform the data according to the techniques that are to be used in the process. Overall, it is impossible to efficiently complete the mining step without altering data to extract potentially valuable patterns.
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To begin with, transforming coded and text data is highly significant for enabling the completion of the data mining procedure. In other words, as this step of data collecting is dependent on techniques that intend to efficiently identify valuable information, the data needs to be transformed beforehand to suit the methods (Alasadi & Bhaya, 2017). To be more exact, data mining as a process is designed to sort the information to leave the unneeded one at this step of forming new knowledge (Alasadi & Bhaya, 2017). This procedure is recognized for its focus on seeking the value to be used in different processes of Knowledge Discovery in Databases (Alasadi & Bhaya, 2017). Therefore, data mining is acceptable for collecting numerous data types considering that information from databases is properly structured for further analysis (Alasadi & Bhaya, 2017). Overall, numerous types of coded or text data are acceptable for data mining if adapted satisfactorily during the process of data transformation.
To conclude, the necessity of data transformation is notable due to data mining employing numerous techniques for efficiently identifying and structuring valuable information. Furthermore, to implement the methods productively, data should be appropriately adopted before the process itself. In contrast, without transforming the data, this step might not bring fruitful outcomes as selected information would not be valuable for further knowledge collecting.
Alasadi, S. A., & Bhaya, W. S. (2017). Review of data preprocessing techniques in data mining. Journal of Engineering and Applied Sciences, 12(16), 4102-4107.