Knowledge discovery in sociological databases: An application on general society survey dataset

International Journal of Crowd Science

Jiangtian Li¹  Jun Zhang²  Lianjun Dai²  Zhiwen Pan³  Yiqiang Chen³  Jesus Pacheco⁴ 

[1] High School Affiliated to Renmin University of China, Beijing, China;Information Centre of China Disabled Persons' Federation, Beijing, China;Institute of Computing Technology Chinese Academy of Sciences, Beijing, China;Universidad de Sonora, Hermosillo, Mexico;

关键词: data management;    data mining;    crowdsourced big data and analytics;    knowledge discovery;   

DOI  :  10.1108/IJCS-09-2019-0023

来源: DOAJ

【 摘 要 】

Purpose – The General Society Survey(GSS) is a kind of government-funded survey which aims at examining the Socio-economic status, quality of life, and structure of contemporary society. GSS data set is regarded as one of the authoritative source for the government and organization practitioners to make data-driven policies. The previous analytic approaches for GSS data set are designed by combining expert knowledges and simple statistics. By utilizing the emerging data mining algorithms, we proposed a comprehensive data management and data mining approach for GSS data sets. Design/methodology/approach – The approach are designed to be operated in a two-phase manner: a data management phase which can improve the quality of GSS data by performing attribute pre-processing and filter-based attribute selection; a data mining phase which can extract hidden knowledge from the data set by performing data mining analysis including prediction analysis, classification analysis, association analysis and clustering analysis. Findings – According to experimental evaluation results, the paper have the following findings: Performing attribute selection on GSS data set can increase the performance of both classification analysis and clustering analysis; all the data mining analysis can effectively extract hidden knowledge from the GSS data set; the knowledge generated by different data mining analysis can somehow cross-validate each other. Originality/value – By leveraging the power of data mining techniques, the proposed approach can explore knowledge in a fine-grained manner with minimum human interference. Experiments on Chinese General Social Survey data set are conducted at the end to evaluate the performance of our approach.

【 授权许可】

Unknown