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How to use PHP to implement cluster analysis and user classification
Introduction:
Cluster analysis is an unsupervised learning method used to group similar objects together in data. In user classification, cluster analysis can help us divide users into different groups based on their attributes or behaviors. This article will introduce how to use PHP to implement cluster analysis and user classification, and give corresponding code examples.
composer require php-ml/php-ml
use PhpmlPreprocessingNormalizer; $normalizer = new Normalizer(); $normalizedDataSet = $normalizer->transform($dataset);
use PhpmlClusteringKMeans; $kmeans = new KMeans(3); $kmeans->train($normalizedDataSet); $clusters = $kmeans->predict($normalizedDataSet);
In the above code, we specify the number of clusters as 3, then train on the standardized data and predict the cluster to which each data point belongs.
$users = []; // 用户数据 $classifiedUsers = []; foreach ($clusters as $index => $cluster) { $classifiedUsers[$cluster][] = $users[$index]; }
In the above code, we put users with the same cluster label into the same category.
foreach ($classifiedUsers as $cluster => $users) { $userCount = count($users); $averageAge = array_sum(array_column($users, 'age')) / $userCount; echo "Cluster $cluster: $userCount users, average age: $averageAge" . PHP_EOL; }
In the above code, we use the array_column function to get the age field in the user list and calculate the average.
Summary:
This article introduces how to use PHP to implement cluster analysis and user classification. Through the steps of preparing data, installing dependent libraries, data preprocessing, cluster analysis and user classification, we can divide users into different groups based on their attributes or behaviors. At the same time, corresponding code examples are given to help readers better understand the implementation process. I hope readers can gain practical knowledge from this article and provide a reference for user classification.
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