Data mining for customer service support
Information and Management
Knowledge management and data mining for marketing
Decision Support Systems - Knowledge management support of decision making
Evaluation of decision trees: a multi-criteria approach
Computers and Operations Research
Post-pruning in decision tree induction using multiple performance measures
Computers and Operations Research
Machine learning methods for microbial source tracking
Environmental Modelling & Software
Environmental Modelling & Software
Non-linear variable selection for artificial neural networks using partial mutual information
Environmental Modelling & Software
Using fuzzy data mining to evaluate survey data from olive grove cultivation
Computers and Electronics in Agriculture
Improved use of continuous attributes in C4.5
Journal of Artificial Intelligence Research
Predicting the potential habitat of oaks with data mining models and the R system
Environmental Modelling & Software
Environmental Modelling & Software
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This study used the C4.5 data mining algorithm to model farmers' crop choice in two watersheds in Thailand. Previous attempts in the Integrated Water Resource Assessment and Management Project to model farmers' crop choice produced large sets of decision rules. In order to produce simplified models of farmers' crop choice, data mining operations were applied for each soil series in the study areas. The resulting decision trees were much smaller in size. Land type, water availability, tenure, capital, labor availability as well as non-farm and livestock income were found to be important considerations in farmers' decision models. Profitability was also found important although it was represented in approximate ranges. Unlike the general wisdom on farmers' crop choice, these decision trees came with threshold values and sequential order of the important variables. The decision trees were validated using the remaining unused set of data, and their accuracy in predicting farmers' decisions was around 84%. Because of their simple structure, the decision trees produced in this study could be useful to analysts of water resource management as they can be integrated with biophysical models for sustainable watershed management.