Score clustering analysis method based on t-SNE

Abstract

The invention provides a score clustering analysis method based on t-SNE. The method comprises the steps: importing original data, carrying out the t-SNE dimension reduction of high-dimensional scoredata, carrying out the K-Means clustering of the score data after the t-SNE dimension reduction, and obtaining a clustering result. According to the method, after the original data is preprocessed, the data in the high-dimensional space is subjected to dimensionality reduction by using the t-SNE algorithm, and then the original data is clustered by using the K-Means algorithm, so that the problemof non-ideal clustering effect caused by over-high data dimensionality is effectively solved. Due to the fact that the distribution characteristics of the high-dimensional data are completely reservedthrough the t-sne dimension reduction method, the clustering result of the high-dimensional data is obtained through reduction of the clustering result of the data after dimension reduction. The superiority of the dimensionality reduction algorithm in the student score analysis algorithm can be seen by comparing the results of dimensionality reduction before clustering and direct clustering of the high-dimensional data.

Description

technical field [0001] The invention relates to the field of score analysis, in particular to a score analysis method of a clustering algorithm. Background technique [0002] Performance analysis is one of the important means for schools to evaluate the learning situation of students in school. Schools generally use indicators such as pass rate, average score, and total score ranking for analysis, hoping to use these indicators to reflect the recent learning status of students in various subjects. However, there is a lack of correlation between the contents reflected by these indicators, and it is difficult to obtain the comprehensive performance of students. Nowadays, with the rapid development of big data analysis technology, schools can use data mining technology and data visualization technology to analyze students' learning more efficiently and accurately. [0003] There are some performance analysis methods based on data mining. The patent "Rough Set-Based Decision-...

AI Technical Summary

Problems solved by technology

In order to solve the problems of multi-subject performance analysis and multi-dimensional data visualization and overcome the deficiencies in the prior art, the present invention proposes a t-SNE-based performance clustering analysis method, which first reduces the dimensionality of students' grades through the t-SNE algorithm and then passes K-Means algorithm clustering to improve the efficiency and effectiveness of multi-subject score analysis

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