Subject selection recommendation method based on K-clustering algorithm

Abstract

The invention relates to the field of information technology, and provides a subject selection recommendation method based on a K-clustering algorithm. According to the invention, the method comprises the steps: acquiring the personal information of an investigated object as sample data; processing the sample data, thereby facilitating the analysis and calculation, and guaranteeing the reliability and reasonability of follow-up subject recommendation; performing feature analysis on the preprocessed sample data through a K-means + + algorithm, obtaining a final clustering center and a cluster where the final clustering center is located, and setting the cluster according to the subject; calculating the Euclidean distance between the sample data and each final clustering center, distributing the sample data to the final clustering center with the nearest Euclidean distance, and obtaining the recommendation subject corresponding to the cluster where the final clustering center is located. According to the method, the personal information of the investigated object is used as the most direct sample data, the real situation of the student is matched, pertinence and purposiveness are achieved, the recommended subject is more reasonable, and schools and the student are helped to avoid blindness during subject selection.

Description

technical field [0001] The present invention relates to the field of information technology, and more specifically, relates to a method for subject selection and recommendation based on a K-clustering algorithm. Background technique [0002] Under the background of the era of continuous updating of network technology and coverage of big data, college students are faced with diversified learning directions. In the face of all kinds of dazzling choices, students generally feel very confused about their own learning direction. Due to factors such as major choice, development prospects, employment situation, etc., many people don't know how to choose a suitable major, and they don't know where they can develop. Once you make a wrong choice, you may be dissatisfied with your major, and even have problems such as weariness and abandonment. Big data analysis plays a positive auxiliary and promoting role in the analysis and research of college students' learning behavior, and the ...

AI Technical Summary

Problems solved by technology

[0004] In order to overcome the defect that the above-mentioned prior art cannot reasonably recommend subjects with pertinence and purpose, the present invention provides a subject selection and recommendation method based on K-clustering algorithm, which can be more targeted according to the personal information of the survey object and purposeful recommendation of reasonable subjects

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