What are the methods of data preprocessing?

Data preprocessing methods include: 1. Data cleaning, which “cleans” the data by filling in missing values, smoothing noise data, identifying or deleting outliers, and resolving inconsistencies; 2. Data integration, Data from multiple data sources are combined and stored uniformly. The process of establishing a data warehouse is actually data integration; 3. Data transformation; 4. Data reduction.

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

Data preprocessing refers to some processing of data before the main processing. For example, before most geophysical area observation data are converted or enhanced, the irregularly distributed measurement network is first converted into a regular network through interpolation to facilitate computer calculations. In addition, for some profile measurement data, such as seismic data preprocessing includes vertical stacking, rearrangement, trace addition, editing, resampling, multi-channel editing, etc.

Methods of data preprocessing

1. Data cleaning

By filling in missing values , smoothing noisy data, “cleaning” the data by identifying or removing outliers and resolving inconsistencies. The main goals are to achieve the following goals: format standardization, abnormal data removal, error correction, and duplicate data removal.

2. Data integration

Data integration routines combine data from multiple data sources and store them uniformly. The process of establishing a data warehouse is actually data integration. .

3. Data transformation

Convert data into a form suitable for data mining through smooth aggregation, data generalization, standardization, etc.

4. Data reduction

The amount of data is often very large during data mining. Mining and analysis on a small amount of data takes a long time. Data reduction technology can Used to obtain a reduced representation of the data set that is much smaller, but still close to maintaining the integrity of the original data, and the result is the same or almost the same as the result before reduction.

Data preprocessing is a popular research aspect of data mining. After all, this is determined by the background of data preprocessing - almost all data in the real world is dirty data.

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