Learning of High Dengue Incidence with Clustering and FP-Growth Algorithm using WHO Historical Data
Databases
2019-02-01 v1
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.
Keywords
Cite
@article{arxiv.1901.11376,
title = {Learning of High Dengue Incidence with Clustering and FP-Growth Algorithm using WHO Historical Data},
author = {Franz Stewart V. Dizon and Stephen Kyle R. Farinas and Reynaldo John Tristan H. Mahinay and Harry S. Pardo and Cecil Jose A. Delfinado},
journal= {arXiv preprint arXiv:1901.11376},
year = {2019}
}