This is an accepted paper at the 3rd IEEE International Conference on Agents (ICA 2018)
This paper applies FP-Growth algorithm in mining fuzzy association rules for a prediction system of dengue. The system mines its rules through input of historic predictor variables for dengue. The rules will be used to build a rule-based classifier to predict the dengue incidence for the next month for the years 2001-2006 in the Philippines. The FP-Growth Algorithm was compared to Apriori Algorithm by Sensitivity, Specificity, PPV, NPV, execution time and memory usage. The results showed that FP-Growth Algorithm is significantly better in execution time, numerically better in memory and comparable in Sensitivity, Specificity PPV and NPV to Apriori Algorithm.
The following default values were used in this research based on the data:
Support: 0.014
Confidence: 0.9
Generate association rules
rules = Apriori.generate_itemsets_rules(data, support, confidence, lift)
Print association rules
Apriori.print_result(rules)
Generate association rules
rules = FPGrowth.generate_patterns_rules(data, support, confidence)
Print association rules
FPGrowth.print_result(rules)
- Reynaldo John Tristan Mahinay Jr.
- Franz Stewart Dizon
- Stephen Kyle Farinas
- Harry Pardo
The results are showed in this link - Comparison Result
Copyright (c) 2018 Reynaldo John Tristan Mahinay Jr., Franz Stewart Dizon, Stephen Kyle Farinas and Harry Pardo
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