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What are the six steps of data mining? In view of this problem, this article introduces the corresponding analysis and answers in detail, hoping to help more partners who want to solve this problem to find a more simple and feasible way.

Data mining is a non-trivial process of obtaining effective, novel, potentially useful, and ultimately understandable patterns from a large amount of data, as follows:

1. Definition problem

2. Prepare data

3. Browse data

4. Generate the model

5. Browse and verify the model

6. Deploy and update models.

Data mining usually requires data collection, data integration, data specification, data cleaning, data transformation, data mining implementation process, pattern evaluation and knowledge representation.

1. Data collection: according to the obtained data, abstract the characteristic information of the data, and store the collected information into the database. Select an appropriate type of data warehouse for data storage and management

two。 Data integration: classifying data from different sources and formats

3. Data specification: when the amount of data and the value of data are relatively large, we can use the specification technology to get the specification representation of the data set, such as (data value-data average) / data variance. this makes the data much smaller but close to the integrity of the original data, and the results of data mining after the specification are basically the same as those before the specification.

4. Data cleaning: some data are incomplete, such as: some have missing values (values do not exist), some contain noise (errors, outliers), and some are inconsistent (such as different units, etc.). We can use tools to clean up the data to get complete, correct and consistent data.

5. Data transformation: through smooth aggregation, data generalization, normalization and other ways to convert data into data sets suitable for data mining.

6. Feature extraction or feature selection: feature extraction is mostly used in computer vision and image processing. Feature selection is to propose irrelevant and redundant features to prevent over-fitting and improve the accuracy of the model. Common methods include PCA and so on.

7. Data mining process: analyze the data information in the data warehouse, select appropriate data mining tools, apply statistical methods, and use the corresponding data mining algorithms. no, no, no.

8. From the business, verify the correctness of the results of data analysis and data mining.

9. Knowledge representation presents the results of data mining to users in a visual way.

This is the answer to the question about what are the six steps of data mining. I hope the above content can be of some help to you. If you still have a lot of doubts to be solved, you can follow the industry information channel to learn more about it.

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