Knowledge discovery in sociological databases: An application on general society survey dataset. Issue 3 (9th December 2019)

• Record Type: Journal Article
• Authors: Pan, Zhiwen; Li, Jiangtian; Chen, Yiqiang; Pacheco, Jesus; Dai, Lianjun; Zhang, Jun
• Is Part Of: International journal of crowd science. Volume 3:Issue 3(2019)
• Page Start: 315
• Page End: 332
• Publication Date: 2019-12-09
• Subjects: Data management -- Data mining -- Crowdsourced big data and analytics -- Knowledge discovery; Human-computer interaction -- Periodicals; Human computation -- Periodicals; Cooperating objects (Computer systems) -- Periodicals; 621.3984
• DOI: 10.1108/IJCS-09-2019-0023
• Languages: English
• ISSNs: 2398-7294

Abstract

Abstract : 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.