K-Means Algorithm Research Overview: International Trends and Applications

K-means algorithm is a classic clustering algorithm widely used in various fields such as image processing, bioinformatics, and network security. Internationally, researchers have extensively studied K-means algorithm, focusing on several key areas:

1. Improving K-means Algorithm Accuracy and Efficiency: Researchers have proposed numerous methods to enhance K-means algorithm, including density-based K-means, accelerated K-means, and clustering-merging K-means.

2. Applications in Image Processing: K-means algorithm has been successfully applied to image segmentation, compression, and retrieval, demonstrating its effectiveness in this field.

3. Applications in Network Security: Researchers have leveraged K-means algorithm for intrusion detection and malware detection, achieving promising results.

4. Applications in Bioinformatics: K-means algorithm has been employed for gene expression data analysis and protein structure prediction, contributing significantly to bioinformatics research.

In summary, research on K-means algorithm internationally has reached a mature and extensive level. This research not only expands the algorithm's application scope but also provides valuable insights for the development of other clustering algorithms.