What is the difference between data mining and data analysis?

Difference: 1. The conclusion drawn by "data analysis" is the result of human intellectual activities, while the conclusion drawn by "data mining" is the knowledge discovered by the machine from the learning set [or training set, sample set] Rules; 2. "Data analysis" cannot establish a mathematical model and requires manual modeling, while "data mining" directly completes mathematical modeling.

#The operating environment of this article: Windows 7 system, Dell G3 computer.

What is the difference between data mining and data analysis?

Data mining is to find hidden rules from massive data. Data analysis generally has a clear goal.

The main difference between data mining and data analysis

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

2. The conclusions drawn by "data analysis" are the results of human intellectual activities, while the conclusions drawn by "data mining" are the knowledge rules discovered by the machine from the learning set (or training set, sample set).

3. The application of "data analysis" to draw conclusions is human intellectual activity, while the knowledge rules discovered by "data mining" can be directly applied to predictions.

4. "Data analysis" cannot establish a mathematical model and requires manual modeling, while "data mining" directly completes mathematical modeling. For example, the essence of traditional cybernetic 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" derived from KDD, given A set of input parameters can produce a set of output quantities.

A simple example:

There are some people who always fail to pay money to telecom operators in time. How to discover them?

Data analysis: Through observation of the data, we found that 82% of the poor people who did not pay money in time accounted for 82%. So the conclusion is that people with low incomes tend to pay late. The conclusion is that tariffs need to be reduced.

Data mining: Discover the deep-seated reasons by yourself through written algorithms. The reason may be that people who live outside the Fifth Ring Road do not pay in time due to the remote environment. The conclusion is that more business halls or self-service payment points need to be set up.

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