What are the statistical methods of data mining?

Traditional statistical methods of data mining include regression analysis, principal component analysis and cluster analysis.

Statistical learning methods of non-machine data mining include fuzzy sets, rough sets and support vector machines.

Data mining refers to the process of finding hidden information from a large number of data through algorithms. 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 and pattern recognition. Nowadays, people are eager to analyze massive data in depth, find and extract the hidden information in order to make better use of these data. It is precisely because of this demand that data mining technology came into being. Data mining has many legitimate uses, such as finding the relationship between drugs and their side effects in the patient database. This relationship may not appear in 1000 people, but this method can reduce the number of patients who have adverse reactions to drugs and may save lives.

Regarding the related learning of data mining, we recommend the related courses of CDA data division. The course content takes 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". Click to make an appointment for a free audition class.