A Bibliometric Assessment on Sustainability and Clustering
Review researches are useful in terms of analysis of research problems. Between the types of review documents, we commonly find bibliometric studies. This type of application often helps the global visualization of a research problem and helps academics worldwide to understand the context of a research area better. In this document, a bibliometric view surrounding clustering techniques and sustainability problems is presented. The authors aimed at which issues mostly use clustering techniques and even which sustainability issue would be more impactful on today’s moment of research. During the bibliometric analysis, we found 10 different groups of research in clustering applications for sustainability issues: Energy; Environmental; Non-urban Planning; Sustainable Development; Sustainable Supply Chain; Transport; Urban Planning; Water; Waste Disposal; and, Others. Moreover, by analyzing the citations of each group, it was discovered that the Environmental group could be classified as the most impactful research cluster in the area mentioned. After the content analysis of each paper classified in the environmental group, it was found that the k-means technique is preferred for solving sustainability problems with clustering methods since it appeared the most amongst the documents. The authors finally conclude that a bibliometric assessment could help indicate a gap of researches on waste disposal – which was the group with the least amount of publications – and the most impactful research on environmental problems.Procedia APA BibTeX Chicago EndNote Harvard JSON MLA RIS XML ISO 690 PDF Downloads 252
 Steiner Neto, P. J.; Datta, D.; Steiner, M. T. A.; Canciglieri Junior, O.; Figueira, J. R.; Detro, S. P.; Scarpin, C. T. (2017) A multi-objective genetic algorithm based approach for the location of grain silos in Paraná State of Brazil. Computers and Industrial Engineering, 111, 381-390.
 Steiner, M. T. A.; Datta, D.; Steiner Neto, P. J.; Scarpin, C. T.; Figueira, R. J. (2014) Multi-objective optimization in partitioning the healthcare system of parana state in Brazil, 52, 53-64.
 Elsevier. (2017). Scopus - Content Coverage Guide. From: https://bit.ly/2w4Q9TQ
 Caravaggio, N., Caravella, S., Ishizaka, A., & Resce, G. (2019). Beyond CO 2 : A multi-criteria analysis of air pollution in Europe. Journal of Cleaner Production, 219, 576–586.
 Stojnić, S., Avramidou, E. V., Fussi, B., Westergren, M., Orlović, S., Matović, B., Trudić, B., Kraigher, H., Aravanopoulos, F. A., & Konnert, M. (2019). Assessment of genetic diversity and population genetic structure of Norway Spruce (Picea abies (L.) Karsten) at its Southern Lineage in Europe. Implications for conservation of forest genetic resources. Forests, 10(3).
 Denton, A. M., & Roy, A. (2018). Cluster-Overlap Algorithm for Assessing Preprocessing Choices in Environmental Sustainability Anne. IEEE International Conference on Big Data, 4212–4220.
 Sahoo, S., Dhar, A., & Kar, A. (2016). Environmental vulnerability assessment using Grey Analytic Hierarchy Process based model. Environmental Impact Assessment Review, 56, 145–154.
 Jiang, Q., Chen, L. C., & Zhang, J. (2019). Perception and preference analysis of fashion colors: Solid color shirts. Sustainability (Switzerland), 11(8).
 Li, Jing, Luo, Y., & Wang, S. (2019). Spatial effects of economic performance on the carbon intensity of human well-being: The environmental Kuznets curve in Chinese provinces. Journal of Cleaner Production, 233, 681–694.
 Lee, K. J., Kahng, H., Kim, S. B., & Park, S. K. (2018). Improving environmental sustainability by characterizing spatial and temporal concentrations of ozone. Sustainability (Switzerland), 10(12).
 Gurumurthy, S., Jin, Y., Yu, L., Li, W., Fang, F., Zhang, C., & Zhang, X. (2018, June 20). Exploiting data and human knowledge for predicting wildlife poaching. Proceedings of the 1st ACM SIGCAS Conference on Computing and Sustainable Societies, COMPASS 2018.
 Li, Z., Han, J., Ding, B., & Kays, R. (2012). Mining periodic behaviors of object movements for animal and biological sustainability studies. Data Mining and Knowledge Discovery, 24(2), 355–386.
 Li, Z., Han, J., Ji, M., Tang, L. A., Yu, Y., Ding, B., Lee, J. G., & Kays, R. (2011). MoveMine: Mining moving object data for discovery of animal movement patterns. ACM Transactions on Intelligent Systems and Technology, 2(4).
 Sanei, A., Zakaria, M., Yusof, E., & Roslan, M. (2011). Estimation of leopard population size in a secondary forest within Malaysia’s capital agglomeration using unsupervised classification of pugmarks. Tropical Ecology, 52(2), 209–217.
 Kidd, A. G., Bowman, J., Lesbarrères, D., & Schulte-Hostedde, A. I. (2009). Hybridization between escaped domestic and wild American mink (Neovison vison). Molecular Ecology, 18(6).
 Fargo, J., & Tyler, A. V. (1991). Sustainability of Flatfish-Dominated Fish Assemblages in Hecate Strait, British Columbia, Canada. In the Netherlands Journal of Sea Research, 27(4).
 Lam, N. S. N., Reams, M., Li, K., Li, C., & Mata, L. P. (2016). Measuring Community Resilience to Coastal Hazards along the Northern Gulf of Mexico. Natural Hazards Review, 17(1).
 Wang, G., & Wu, W. (2007). Spatial distribution of ecological security status assessment of West-Liaohe River based on geographic information system. Frontiers of Environmental Science and Engineering in China, 1(4), 471–476.