Five aspects of the impact of mining on data mining results

First:

Data type,

Different attributes of an object are described by different data types, such as age --> int; birthday --> date. Different types of data mining must be treated differently.

Second:

Data quality,

Data quality directly affects the quality of the mining results. Generally, noise, outlier, data omission, and duplication in data must be solved.

Third:

Data Mining preprocessing steps,

Data must be processed before data mining to adapt the data to the mining technology and improve the data quality, such as converting continuous values

Discrete values (turning age into middle age, old age, young people, and teenagers) to adapt to mining techniques, such as reducing the number of object attributes.

Fourth:

Analyze Data Based on the relationship between data,

One method of data analysis is to find out the relationship between data, and then use this relationship instead of data for subsequent analysis, such as nearby persons:

When the distance between users is obtained, it is determined whether the distance is nearby or not.

 

Five aspects of the impact of mining on data mining results