Application of the intra cluster, characteristic of k-means clustering method in English score analysis in Colleges

• Published in: Journal of physics. Conference series Vol. 1941; no. 1; pp. 12001 - 12007
• Main Author: Wang, Qin
• Format: Journal Article
• Language: English
• Published: Bristol IOP Publishing 01.06.2021
• Subjects: Algorithms; Cluster analysis; Clustering; Colleges & universities; Data analysis; Data mining; Image classification; Image segmentation; Machine learning; Physics; Vector quantization
• ISSN: 1742-6588; 1742-6596; 1742-6596
• DOI: 10.1088/1742-6596/1941/1/012001

As an important data mining method, clustering algorithm can find valuable information hidden in data objects through unsupervised classification method. It is widely used in data analysis, image segmentation, feature learning and other fields. It is one of the common methods in traditional machine learning algorithm. K-means clustering algorithm is a partition-based algorithm in clustering analysis, which is efficient and accurate, easy to implement, and is generally implemented in machine learning and other fields. In this paper, based on the characteristics of K-means clustering method, the scores of each part of 30 college students' English scores are used as the input data, and the cluster center is used as the influencing factor to participate in the clustering process. The clustering effect is evaluated under the average distance of the characteristics within the cluster after several iterations.