rjtmahinay/fuzzy-association-rule-mining

Comparison of Apriori and FP-Growth Algorithm in accuracy metrics, execution time and memory usage for a prediction system of dengue.

Learning of High Dengue Incidence with Clustering and FP-Growth Algorithm using WHO Historical Data

This is an accepted paper at the 3rd IEEE International Conference on Agents (ICA 2018)

Abstract

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.

Rule Mining Usage

The following default values were used in this research based on the data:

Support: 0.014
Confidence: 0.9

Apriori Algorithm

Generate association rules

rules = Apriori.generate_itemsets_rules(data, support, confidence, lift)

Print association rules

Apriori.print_result(rules)

FP-Growth Algorithm

Generate association rules

rules = FPGrowth.generate_patterns_rules(data, support, confidence)

Print association rules

FPGrowth.print_result(rules)

Authors

• Reynaldo John Tristan Mahinay Jr.
• Franz Stewart Dizon
• Stephen Kyle Farinas
• Harry Pardo

Results

The results are showed in this link - Comparison Result

License

Copyright (c) 2018 Reynaldo John Tristan Mahinay Jr., Franz Stewart Dizon, Stephen Kyle Farinas and Harry Pardo

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