Shahadat Hossain
ABSTRACT
The perception of corruption in any country is the most crucial aspect for its catastrophic effects on other vital indices, such as education index, ladder score, and GDP. This paper provides an analytical analysis of a collective dataset that identifies changes in perceptions of corruption using various comparable indicators. We aggregate information from many open-source data repositories to create a collective dataset. Furthermore, we conduct a detailed investigation of the elements that influence the perception of corruption in every country. In addition, we form clusters using the density-based spatial clustering of applications with noise (DBSCAN) approach and present our findings. We discover a significant relationship among the education index, GDP, ladder score, and social support based on corruption perception.
- Hannes Baumann. 2020. The corruption perception index and the political economy of governing at a distance. International Relations 34, 4 (2020), 504–523.Google Scholar
Cross Ref
- Agyenim Boateng, Yan Wang, Collins Ntim, and Keith W Glaister. 2021. National culture, corporate governance and corruption: A cross-country analysis. International Journal of Finance & Economics 26, 3 (2021), 3852–3874.Google ScholarCross Ref
- Bill De Maria. 2008. Neo-colonialism through measurement: a critique of the corruption perception index. Critical perspectives on international business (2008).Google Scholar
- P Drogovoz, O Yusufova, N Kashevarova, and V Shiboldenkov. 2019. Exploratory data analysis of national indicators referred to scientific and technological development and to economic growth. In AIP Conference Proceedings, Vol. 2171. AIP Publishing LLC, 080003.Google ScholarCross Ref
- IBM Cloud Education. [n.d.]. Exploratory Data Analysis. https://www.ibm.com/cloud/learn/exploratory-data-analysis. Accessed: 2021-07-12.Google Scholar
- Martin Ester, Hans-Peter Kriegel, Jörg Sander, and Xiaowei Xu. 1996. A Density-Based Algorithm for Discovering Clusters in Large Spatial Databases with Noise. In Proceedings of the Second International Conference on Knowledge Discovery and Data Mining (KDD-96), Portland, Oregon, USA, Evangelos Simoudis, Jiawei Han, and Usama M. Fayyad (Eds.). AAAI Press, 226–231.Google ScholarDigital Library
- Sanja Filipović, Mirjana Radovanović, and Vladimir Golušin. 2018. Macroeconomic and political aspects of energy security–Exploratory data analysis. Renewable and Sustainable Energy Reviews 97 (2018), 428–435.Google ScholarCross Ref
- Nafi Ghaniy, Fithra Faisal Hastiadi, 2017. Political, social and economic determinants of corruption. International Journal of Economics and Financial Issues 7, 4(2017), 144–149.Google Scholar
- Stuart C Gilman. 2018. To understand and to misunderstand how corruption is measured: academic research and the corruption perception index. Public Integrity 20, sup1 (2018), S74–S88.Google Scholar
- Yujin Jeong and Robert J Weiner. 2012. Who bribes? Evidence from the United Nations’ oil-for-food program. Strategic Management Journal 33, 12 (2012), 1363–1383.Google ScholarCross Ref
- United Nation Office on Drug and Crime. [n.d.]. Office on Drugs and Crime. https://dataunodc.un.org/. Accessed: 2021-07-12.Google Scholar
- Christian Posse. 1995. Projection pursuit exploratory data analysis. Computational Statistics & data analysis 20, 6 (1995), 669–687.Google Scholar
- Myrian Raquel Noguera Salinas, Maria Claudia Figueiredo Pereira Emer, and Adolfo Gustavo Serra Seca Neto. 2019. Short datathon for the interdisciplinary development of data analysis and visualization skills. In 2019 IEEE/ACM 12th International Workshop on Cooperative and Human Aspects of Software Engineering (CHASE). IEEE, 95–98.Google ScholarDigital Library
- Ajaypal Singh. [n.d.]. World Happiness Report 2021. https://www.kaggle.com/ajaypalsinghlo/world-happiness-report-2021. Accessed: 2021-06-21.Google Scholar
- Roberta Troisi and Gaetano Alfano. 2020. Firms’ crimes and land use in Italy. An exploratory data analysis. In INTERNATIONAL SYMPOSIUM: New Metropolitan Perspectives. Springer, 749–758.Google Scholar
- Nagehan Uca, Hüseyin İnce, and Halefsan Sümen. 2016. The mediator effect of logistics performance index on the relation between corruption perception index and foreign trade volume. (2016).Google Scholar
- Nico Verbeeck, Richard M Caprioli, and Raf Van de Plas. 2020. Unsupervised machine learning for exploratory data analysis in imaging mass spectrometry. Mass spectrometry reviews 39, 3 (2020), 245–291.Google Scholar
- Wikipedia. [n.d.]. World Happiness Report. https://en.wikipedia.org/wiki/World_Happiness_Report. Accessed: 2021-07-12.Google Scholar
- Azzouz Zouaoui, Anas Al Qudah, and Mounira Ben-Arab. 2017. World corruption perception index analysis. Research Journal of Finance and Accounting 8, 24 (2017).Google Scholar
Index Terms
- Ramifications of Corruption Perception Index: An Exploratory Data Analyses using DBSCAN
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