| 摘 要: 本文基于K-means算法对网络招聘数据进行聚类分析，并运用关联规则对大数据和IT行业进行关联预 测。从分析结果可知，学历和经验直接影响薪资水平，且金融银行职业类型的平均薪资水平在所得分类中最高，同时也 得到大数据和IT行业对学历要求较高，其占总体职业类型比例有增加趋势。 |
| 关键词: 网络招聘；数据挖掘；聚类算法；关联度分析 |
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中图分类号: TP311
文献标识码: A
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| 基金项目: 河北省自然科学基金项目(A2015203121)，燕山大学国家级大学生创新项目(201610216027). |
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| Data Mining of Online Recruitment Information Based on K-Means and Correlation Analysis |
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ZHANG Yin,ZHAO Wenhui,BAO Hengyue,LI Yajian,ZHOU Keqiang
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( Yanshan University, Qinhuangdao 066004, China)
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| Abstract: Based on the k-means algorithm,the paper conducts cluster analysis of the online recruitment data,and adopts the association rules to predict big data and the IT industry.The analysis results show that education background and work experience directly influences the salary,the average salary in the financial and banking industry is higher than all the other professions,the educational requirements are relatively high in the big data and IT industry,and there is a growing tendency of its proportion in all the professions. |
| Keywords: online recruitment;data mining;clustering algorithm;correlation analysis |
