Abstract
The purpose of this study is to use data mining techniques for the exploratory analysis of a database of ichthyoplankton samples from a freshwater reservoir in Legal Amazon. This database has already been analyzed using statistical techniques, but these did not find a relationship between biotic and abiotic factors. The application of the Apriori algorithm allows us to generate association rules that yield an understanding of the process of fish spawning in Tocantins River. In this case, we demonstrate the effective use of data mining for the discovery of patterns and processes in ecological systems, and suggest that statistical methods often used by ecologists can be coupled with data mining techniques to generate hypotheses.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Preview
Unable to display preview. Download preview PDF.
References
Hochachka, W.M., Caruana, R., Fink, D., Munson, A., Riedewald, M., Sorokina, D., Kelling, S.: Datamining discovery of pattern and process in ecological systems. Journal of Wildlife Management 71(7), 2427–2437 (2007)
Breiman, L.: Bagging predictors. Journal Machine Learning 24, 123–140 (1996)
Bauer, E., Kohavi, R.: An empirical comparison of voting classification algorithms: bagging, boosting, and variants. Journal Machine Learning 36, 105–139 (1999)
Hastie, T., Tibshirani, R., Friedman, J.: The elements of statistical learning: data mining, inference, and prediction, 2nd edn. Springer, New York (2009)
Agrawal, R., Srikant, R.: Fast Algorithms for Mining Association Rules in Large Databases. In: Proceedings of the 20th International Conference on Very Large Data Bases (VLDB 1994), pp. 487–499 (1994)
Agostinho, A.A., Marques, E.E., Agostinho, C.S., Almeida, D.A., Oliveira, R.J., Melo, J.R.B.: Fish ladder of Lajeado Dam: migrations on oneway routes? Neotropical Ichthyology 5(2), 121–130 (2007)
Empresa de pesquisa energética – EPE: Plano Decenal de Expansão de Energia 2021. MME/EPE, BrasÃlia (2012)
Nakatani, K., Agostinho, A.A., Baumgartner, G., Bialetzki, A., Sanches, P.V., Makrakis, M.C., Pavanelli, C.S.: Ovos e larvas de peixes de água doce: desenvolvimento e manual de identificação. EDUEM. Maringá, 378 p. (2001)
Tanaka, S.: Stock assessment by means of ichthyoplankton surveys. FAO Fisheries Technical Paper, vol. 122, pp. 33–51 (1973)
Agrawal, R., Imielinski, T., Swami, A.: Database Mining: A Performance Perspective. IEEE Transactions on Knowledge and Data Engineering, Special Issue on Learning and Discovery in Knowledge-Based Databases 5, 914–925 (1993)
Geng, L., Hamilton, H.J.: Interestingness measures for data mining: A survey. ACM Computing Surveys 38(3), Article 9, 9–es (2006)
Wu, X., Kumar, V., Quinlan, J.R., Ghosh, J., Yang, Q., Motoda, H., McLachlan, G.J., Ng, A., Liu, B., Yu, P.S., Zhou, Z., Steinbach, M., Hand, D.J., Steinberg, D.: Top 10 algorithms in data mining. Knowledge and Information Systems 14(1), 1–37 (2007)
Hall, M., Frank, E., Holmes, G., Pfahringer, B., Reutemann, P., Witten, I.H.: The WEKA Data Mining Software: An Update. SIGKDD Explorations 11(1), 10–18 (2009)
Han, J., Kamber, M., Pei, J.: Data Mining: Concepts and Techniques, 3rd edn. Morgan Kaufmann Publishers Inc., San Francisco (2011)
Hall, M.A.: Correlation-based Feature Selection for Machine Learning. Ph.D thesis, Waikato University, Hamilton, NZ (1998)
Tetko, I.V., Solov’ev, V.P., Antonov, A.V., Yao, X., Doucet, J.P., Fan, B., Hoonakker, F., Fourches, D., Jost, P., Lachiche, N., Varnek, A.: Benchmarking of Linear and Nonlinear Approaches for Quantitative Structure−Property Relationship Studies of Metal Complexation with Ionophores. Journal of Chemical Information and Modeling 46, 808–819 (2006)
Ghiselli, E.E.: Theory of psychological measurement. McGraw-Hill (1964)
Hall, M.A.: Correlation-based feature selection for discrete and numeric class machine learning. In: ICML 2000 Proceedings of the Seventeenth International Conference on Machine Learning, pp. 359–366 (2000)
Witten, I.H., Frank, E., Hall, M.A.: Data Mining: Practical Machine Learning Tools and Techniques, 3rd edn. Morgan Kaufmann Publishers Inc., San Francisco (2011)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2013 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
de A.Silva, M., Trevisan, D.Q., Prata, D.N., Marques, E.E., Lisboa, M., Prata, M. (2013). Exploring an Ichthyoplankton Database from a Freshwater Reservoir in Legal Amazon. In: Motoda, H., Wu, Z., Cao, L., Zaiane, O., Yao, M., Wang, W. (eds) Advanced Data Mining and Applications. ADMA 2013. Lecture Notes in Computer Science(), vol 8347. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-53917-6_34
Download citation
DOI: https://doi.org/10.1007/978-3-642-53917-6_34
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-53916-9
Online ISBN: 978-3-642-53917-6
eBook Packages: Computer ScienceComputer Science (R0)