Mining association rules with multiple minimum supports
KDD '99 Proceedings of the fifth ACM SIGKDD international conference on Knowledge discovery and data mining
Data mining: concepts and techniques
Data mining: concepts and techniques
Knowledge Engineering: Concepts and Practices for Knowledge Base Systems
Knowledge Engineering: Concepts and Practices for Knowledge Base Systems
K-means clustering via principal component analysis
ICML '04 Proceedings of the twenty-first international conference on Machine learning
Combining in situ flow cytometry and artificial neural networks for aquatic systems monitoring
Expert Systems with Applications: An International Journal
| Hi-index | 12.05 |
Since almost all anthropogenic activities ultimately affect the coastal waters, access properties and processes in this environment is the major issue in decision making and system management. Particularly, seasonal patterns are not clear in tropical areas, therefore, requiring environmental classification. The knowledge of long-term biogenic element dynamics, the biological response, and the selection of indicators connecting lower and higher trophic levels have became a real need for the sustainable management of marine resources. Under this scenario, this paper uses a machine-learning approach to determine the ecological status of coastal waters based on patterns of occurrence of meroplankton larvae of epibenthic fauna and its relationship with other environmental variables. The case studied is the upwelling influenced bay at Cabo Frio Island (Rio de Janeiro - Brazil) because this location has been suffering with anthropogenic impact. Models of crisp and fuzzy rules have been tested as classifiers. Results show it is possible to access hidden patterns of water masses within a set of association rules.