What is the difference between traditional data mining and big data?

Big data has the characteristics of "high-dimensional, massive and real-time", that is to say, it has the characteristics of large amount of data, high data source and data dimension, and fast update speed. Traditional data mining technology may be difficult to solve, so it is necessary to improve the processing ability from the aspects of algorithm improvement and scheme framework.

Based on database theory, machine learning, artificial intelligence and modern statistics, data mining has been applied in many fields. It involves many algorithms, such as neural network and decision tree derived from machine learning, support vector machine based on statistical learning theory, classification regression tree, correlation analysis and so on. The definition of data mining is to discover meaningful patterns or knowledge from massive data. If you want to master all the relevant knowledge, you must study systematically. It is recommended to enroll in online or offline courses of relevant professional institutions.

To learn more about data mining, you can take a look at the course of CDA Data Analyst. 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 select business process optimization tools or algorithm tools through proximate cause analysis and macro root analysis, instead of "adjusting algorithm packages when encountering problems", and click to make an appointment for a free audition class.