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The editor will share with you the differences between data mining and data analysis. I hope you will gain a lot after reading this article. Let's discuss it together.

Differences: 1. The conclusion drawn by "data analysis" is the result of human intellectual activities, while the conclusion drawn by "data mining" is the knowledge rules discovered by machines from learning sets (or training sets, sample sets). 2. "data analysis" can not establish a mathematical model, so it needs manual modeling, while "data mining" directly completes the mathematical modeling.

Data mining is to find hidden rules from massive data, and the goal of data analysis is generally clear.

Main differences between data Mining and data Analysis

1. The focus of "data analysis" is to observe data, while the focus of "data mining" is to find "knowledge rules" KDD (Knowledge Discover in Database) from data.

2. The conclusion of "data analysis" is the result of human intellectual activities, while the conclusion of "data mining" is the knowledge rules discovered by machines from learning sets (or training sets, sample sets).

3. The application of the conclusion of "data analysis" is human intellectual activity, and the knowledge rules discovered by "data mining" can be directly applied to prediction.

4. "data analysis" can not establish a mathematical model and needs manual modeling, while "data mining" directly completes the mathematical modeling. For example, the essence of traditional cybernetics modeling is to describe the functional relationship between input variables and output variables. "data mining" can automatically establish the functional relationship between input and output through machine learning. According to the "rules" obtained by KDD, given a set of input parameters, a set of outputs can be obtained.

Take a simple example:

There are some people who always fail to pay money to telecom operators in time, how to find them?

Data analysis: through the observation of the data, we found that the poor accounted for 82% of the people who did not pay in time. So the conclusion is that people with low income tend to pay their fees late. The conclusion is that the tariff needs to be reduced.

Data mining: discover the deep-seated reasons by writing algorithms. The reason may be that people who live outside the Fifth Ring Road do not pay in time because of the remote environment. Conclusion it is necessary to set up more business halls or self-payment points.

After reading this article, I believe you have a certain understanding of the differences between data mining and data analysis. If you want to know more about it, you are welcome to follow the industry information channel. Thank you for reading!

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