Java implementation code example of K-means clustering algorithm

1. Introduction

K-means clustering algorithm randomly selects K objects as the initial clustering center. Then, the distance between each object and each seed cluster center is calculated, and each object is assigned to the nearest cluster center. Cluster centers and the objects assigned to them represent a cluster. Once all objects are assigned, the cluster center of each cluster will be recalculated according to the existing objects in the cluster. This process will be repeated until a termination condition is met. The termination condition can be that no (or minimum number) objects are reassigned to different clusters, no (or minimum number) cluster centers change again, and the sum of error squares is locally minimum.

2. What is clustering

Clustering is a process of classifying and organizing data members who are similar in some aspects. Clustering is a technology to discover this internal structure. Clustering technology is often called unsupervised learning.

3. What is K-means clustering

K-means clustering is the most famous partition clustering algorithm. Because of its simplicity and efficiency, it has become the most widely used of all clustering algorithms. Given a set of data points and the required number of clusters K, K is specified by the user, and the k-means algorithm repeatedly divides the data into K clusters according to a distance function.

4. Realization

The Java code is as follows:

5. Description:

The specific code is found on the Internet. According to your own understanding, comments are added and some modifications are made. If the comments are wrong, please correct them

6. Test

Summary: the test code has passed. The clustering results are checked, and the results basically meet the requirements. As for whether there is a more accurate algorithm to be found. The specific practice needs to be excavated

summary

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