The correct answer is: Data summarization.
Data summarization is the process of reducing the volume of data while retaining meaningful information. This can be done in a number of ways, such as by removing duplicate data, identifying and removing outliers, and grouping similar data together. Data summarization can be used to improve the performance of data analysis tasks, such as data mining and machine learning.
Data integration is the process of combining data from multiple sources into a single, consistent data set. This can be done in a number of ways, such as by using data warehouses, data marts, and data federation. Data integration is often used to improve the accuracy and completeness of data.
Data compression is the process of reducing the size of data without losing any of its original information. This can be done in a number of ways, such as by using lossless compression algorithms or lossy compression algorithms. Data compression is often used to improve the performance of data storage and transmission.
Data augmentation is the process of artificially increasing the size of a data set by creating new data points that are similar to the existing data points. This can be done in a number of ways, such as by using data generation techniques or data augmentation techniques. Data augmentation is often used to improve the performance of machine learning algorithms.
In conclusion, data summarization is the process of reducing the volume of data while retaining meaningful information. This can be done in a number of ways, such as by removing duplicate data, identifying and removing outliers, and grouping similar data together. Data summarization can be used to improve the performance of data analysis tasks, such as data mining and machine learning.