The common methods of data mining are:

Neural Network Methods

Neural networks are very suitable for solving the problems of data mining due to their own good robustness, self-organization and adaptability, parallel processing, distributed storage and highly fault-tolerant properties, so they have received more and more attention in recent years.

Genetic Algorithm

Genetic Algorithm is a stochastic search algorithm based on biological natural selection and hereditary mechanism, which is a bionic global optimization method. Genetic algorithms have properties such as implicit parallelism and ease of combination with other models that make it used in data mining.

Decision Tree Methods

Decision tree is an algorithm commonly used in predictive modeling, which classifies large amounts of data in a purposeful way to find some valuable, potential information from it. Its main advantages are simple description, fast categorization, especially suitable for large-scale data processing.

Rough set methods

Rough set theory is a mathematical tool for studying imprecise and uncertain knowledge. Rough set methods have several advantages: no additional information needs to be given; simplify the expression space of the input information; simple algorithms, easy to operate. Rough sets deal with objects that are similar to two-dimensional relational tables of information tables.

Covering Positive Examples and Excluding Counter-Examples Method

It utilizes the idea of covering all positive examples and excluding all counter-examples to find rules. First select any seed in the set of positive examples and go to the set of counterexamples and compare them one by one. If it is compatible with the selector formed by the value of the field, it is discarded, and if it is not, it is retained. Cycling through all the positive example seeds according to this idea will result in the rule for the positive example (the combined formula of the selectors).

Statistical Analysis Methods

There are two kinds of relationships between database field items: functional and correlation relationships, and their analysis can be done by statistical methods, i.e., by using the principles of statistics to analyze the information in the database. Common statistics, regression analysis, correlation analysis, difference analysis, etc. can be performed.

Fuzzy set method

That is, the use of fuzzy set theory to carry out fuzzy judgment, fuzzy decision-making, fuzzy pattern recognition and fuzzy clustering analysis of practical problems. The higher the complexity of the system, the stronger the fuzziness, and the general fuzzy set theory is to use the degree of affiliation to portray the fuzzy things also this and that.