In essence, data mining generally refers to the process of finding hidden information from a large number of data through algorithms. Text mining is sometimes called text mining, text data mining, etc. , roughly equivalent to text analysis, generally refers to the production of high-quality information in the process of text processing.

Data mining is usually related to computer science, and the above goals are achieved through statistics, online analytical processing, information retrieval, machine learning, expert system (relying on past empirical rules), pattern recognition and other methods. In text mining, high-quality information is usually generated by classification and prediction, such as pattern recognition. Text mining usually involves processing the input text (usually analyzing, adding some derived language features and eliminating noise, and then inserting it into the database), generating structured data, and finally evaluating and interpreting the output.

Regarding the related learning of data mining and text mining, we recommend the related courses of CDA data division, which take into account the cultivation of horizontal ability to solve data mining process problems and vertical ability to solve data mining algorithm problems. Students are required to think from the root of data governance, explore business problems through digital working methods, and then choose business process optimization tools or algorithm tools through proximate cause analysis and macro root cause analysis, instead of "adjusting algorithm packages when encountering problems". Really understand business thinking and project thinking, and can solve problems. Click to make an appointment for a free audition class.