Search results

1 – 2 of 2
Open Access
Article
Publication date: 9 December 2019

Zhiwen Pan, Jiangtian Li, Yiqiang Chen, Jesus Pacheco, Lianjun Dai and Jun Zhang

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…

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.

Details

International Journal of Crowd Science, vol. 3 no. 3
Type: Research Article
ISSN: 2398-7294

Keywords

Open Access
Article
Publication date: 13 November 2018

Zhiwen Pan, Wen Ji, Yiqiang Chen, Lianjun Dai and Jun Zhang

The disability datasets are the datasets that contain the information of disabled populations. By analyzing these datasets, professionals who work with disabled populations can…

1279

Abstract

Purpose

The disability datasets are the datasets that contain the information of disabled populations. By analyzing these datasets, professionals who work with disabled populations can have a better understanding of the inherent characteristics of the disabled populations, so that working plans and policies, which can effectively help the disabled populations, can be made accordingly.

Design/methodology/approach

In this paper, the authors proposed a big data management and analytic approach for disability datasets.

Findings

By using a set of data mining algorithms, the proposed approach can provide the following services. The data management scheme in the approach can improve the quality of disability data by estimating miss attribute values and detecting anomaly and low-quality data instances. The data mining scheme in the approach can explore useful patterns which reflect the correlation, association and interactional between the disability data attributes. Experiments based on real-world dataset are conducted at the end to prove the effectiveness of the approach.

Originality/value

The proposed approach can enable data-driven decision-making for professionals who work with disabled populations.

Details

International Journal of Crowd Science, vol. 2 no. 2
Type: Research Article
ISSN: 2398-7294

Keywords

1 – 2 of 2