Aplicando algoritmos de clusterização para encontrar inconsistências em bases de dados fiscais
Resumo
Ainda que os dados sobre a propriedade estejam cada vez mais digitalizados, os registros ainda incluem informações desatualizadas advindas de bases de dados historicamente inconsistentes. Os registros de propriedade antigos são automaticamente inseridos em novos formatos digitais, ficando em conflito com campos e bases atuais mais padronizadas. Tal é o caso do banco de imóveis de Fortaleza, onde este estudo se baseia. Esse artigo apresenta como algoritmos de agrupamento podem ajudar a encontrar inconsistências nas bases prediais. Os resultados estimam que 2.048 registros prediais estejam inconsistentes, necessitando limpeza e correção.Referências
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Aprilia, H. and Agustiani, D. (2021). Application of data mining using the k-means algorithm in rural and urban land and building tax (pbb-p2) receivables data in bantul regency. Journal of Physics: Conference Series, 1823:012063.
Bishop, C. M. (2007). Pattern recognition and machine learning.
Carusi, C. and Bianchi, G. (2019). Scientific community detection via bipartite scholar/journal graph co-clustering. Journal of Informetrics, 13(1):354–386.
Davies, D. L. and Bouldin, D. W. (1979). A cluster separation measure. IEEE Trans. Pattern Anal. Mach. Intell., 1(2):224–227.
Eguino, H., Erba, D., Da Silva, E., De Oliveira, A., Piumetto, M., Iturre, T., and Rodríguez, A. (2020). Catastro, valoración inmobiliaria y tributación municipal: Experiencias para mejorar su articulación y efectividad. Informe del Banco Interamericano de Desarrollo (BID).
Ester, Martin ; Kriegel, H.-P. S. J. . X. X. (1996). A density-based algorithm for discovering clusters in large spatial databases with noise. Kdd, 96(34):226—-231.
Ficklin, S., Dunwoodie, L., Poehlman, W., Watson, C., Roche, K., and Feltus, F. (2017). Discovering condition-specific gene co-expression patterns using gaussian mixture models: A cancer case study. Scientific Reports, 7:5.
Geyer, P., Schlüter, A., and Cisar, S. (2017). Application of clustering for the development of retrofit strategies for large building stocks. Advanced Engineering Informatics, 31:32–47.
Grubesic, T. H., Wei, R., and Murray, A. T. (2014). Spatial clustering overview and comparison: Accuracy, sensitivity, and computational expense. Annals of the Association of American Geographers, 104(6):1134–1156.
Han, J., Kamber, M., and Pei, J. (2011). Data Mining: Concepts and Techniques. Morgan Kaufmann Publishers Inc., San Francisco, CA, USA, 3rd edition.
Işeri, O. and Gursel Dino, I. (2022). Building Archetype Characterization Using K-Means Clustering in Urban Building Energy Models, pages 222–236.
Jordan, M. I. and Mitchell, T. M. (2015). Machine learning: Trends, perspectives, and prospects. Science, 349(6245):255–260.
Kriegel, Hans-Peter; Kröger, P. S. J. Z. A. (2011). Density-based clustering. WIREs Data Mining and Knowledge Discovery, 26.
Leung, K. and Leckie, C. (2005). Unsupervised anomaly detection in network intrusion detection using clusters. In Proceedings of the Twenty-eighth Australasian conference on Computer Science-Volume 38, pages 333–342.
Medda, F. R. (2011). 04land value finance: Resources for public transport. Innovative land and property taxation, page 42.
Pu, G., Wang, L., Shen, J., and Dong, F. (2020). A hybrid unsupervised clustering-based anomaly detection method. Tsinghua Science and Technology, 26(2):146–153.
Ranalli, M. and Rocci, R. (2014). Mixture models for ordinal data: a pairwise likelihood approach. Statistics and Computing, 26.
Thiprungsri, S. and Vasarhelyi, M. A. (2011). Cluster analysis for anomaly detection in accounting data: An audit approach. International Journal of Digital Accounting Research, 11.
Xu, Rui; Wunsch, D. (2005). Survey of clustering algorithms. Kdd, 16(3):645—-678.
Zhang, T., Ramakrishnan, R., and Livny, M. (1996). Birch: An efficient data clustering method for very large databases. In Proceedings of the 1996 ACM SIGMOD International Conference on Management of Data, SIGMOD ’96, page 103–114, New York, NY, USA. Association for Computing Machinery.
Publicado
06/08/2023
Como Citar
QUEIROZ, Virginia; FURTADO, Lara Sucupira; PINHEIRO, Vládia Celia.
Aplicando algoritmos de clusterização para encontrar inconsistências em bases de dados fiscais. In: BRAZILIAN WORKSHOP ON ARTIFICIAL INTELLIGENCE IN FINANCE (BWAIF), 2. , 2023, João Pessoa/PB.
Anais [...].
Porto Alegre: Sociedade Brasileira de Computação,
2023
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p. 120-131.
DOI: https://doi.org/10.5753/bwaif.2023.230762.
