Java uses preprocessing such as Mahout data normalization

Maven coordinates of dependent class libraries: <dependency> <groupId>org.apache.mahout</groupId> <artifactId>mahout-mr</artifactId> <version>0.13.0</version> </dependency> Mahout is a Java class library for large-scale machine learning, providing many algorithms and tools for tasks such as data mining, recommendation systems, clustering, classification, and regression. It is widely used for processing and analyzing large-scale datasets, with characteristics of parallelism and scalability. When using Mahout to preprocess data, we can use the 'org. apache. math. stats. DescriptiveStatistics' class to perform data normalization preprocessing operations. import org.apache.mahout.math.stats.DescriptiveStatistics; public class DataNormalizationExample { public static void main(String[] args) { //Sample Dataset double[] data = {1, 2, 3, 4, 5}; //Create a DescriptiveStatistics object DescriptiveStatistics stats = new DescriptiveStatistics(); //Add data to statistical objects for (double value : data) { stats.addValue(value); } //Obtain maximum and minimum values double min = stats.getMinValue(); double max = stats.getMaxValue(); //Normalize data for (int i = 0; i < data.length; i++) { data[i] = (data[i] - min) / (max - min); } //Print normalized data for (double value : data) { System.out.println(value); } } } The above example code demonstrates how to use the Mahout library to normalize data. Firstly, we created a 'DescriptiveStatistics' object and added data to it. Next, we use the 'getMinValue()' and 'getMaxValue()' methods to obtain the minimum and maximum values of the dataset. Then, we normalize the data and print the results. Summary: Mahout is a powerful Java class library that provides many algorithms and tools for large-scale machine learning tasks. When performing data preprocessing, Mahout's' DescriptiveStatistics' class can be used for data normalization operations to ensure that the data is within the same scale range. Normalizing the data may help improve the performance and accuracy of the algorithm.