Affiliation:
1. Foreign Language Department, Shanghai Xingjian College, Shanghai 200072, China
Abstract
In order to integrate and utilize alumni resources in a better way, big data is utilized to construct alumni information analysis model based on improved hierarchical clustering algorithm, so as to realize mining and retrieval of alumni information. First,the basic principle of hierarchical clustering algorithm is analyzed concretely. Moreover, the improvement is performed on this basis, and a method of calculating the distance between class clusters based on the ant colony optimization is proposed, which uses the shortest distance of the ant colony algorithm to optimally solve the distance between hierarchical class clusters, so as to improve the clustering accuracy. Then, the alumni information analysis model based on improved hierarchical clustering algorithm is constructed, and the model is divided into text preprocessing, keyword extraction, text feature vector generation, name disambiguation, and alumni recognition modules. Finally, the improved hierarchical clustering algorithm and construction of model are verified by experiments. The results show that the accuracy of the improved agglomerative hierarchical clustering algorithm is as high as 86.4% on average and 3.8% and 4.8% more than the two traditional algorithms. Thus, the clustering effect of the algorithm is better, and the proposed alumni analysis model can effectively process text disambiguation of web pages and identification of alumni information, which has certain effectiveness.
Funder
Research Project on the Work of the Communist Youth League in Shanghai Schools in 2020, the Research on Tracking Service Mechanism of the Student Leaders after Graduate by Searching the Past Ones
Subject
General Engineering,General Mathematics
Cited by
2 articles.
订阅此论文施引文献
订阅此论文施引文献,注册后可以免费订阅5篇论文的施引文献,订阅后可以查看论文全部施引文献