%0 Conference Paper
%A Mathulamuthu, S.S.
%A Asirvadam, V.S.
%A Dass, S.C.
%A Gill, B.S.
%D 2017
%F scholars:8669
%I Institute of Electrical and Electronics Engineers Inc.
%K Artificial intelligence; Learning systems; Process control; Regression analysis, Averaged silhouettes; Dengue Incidences; K-means clustering; K-means clusters; Machine learning techniques; Regression; Unsupervised techniques; World Health Organization, Climate models
%P 64-69
%R 10.1109/SPC.2016.7920705
%T Cluster based regression model on dengue incidence using dual climate variables
%U https://khub.utp.edu.my/scholars/8669/
%X Dengue fever is one of the major health related issues as reported in World Health Organization (WHO). Therefore, a study is needed on the factors that influencing dengue incidences. This paper presents the influence of dengue incidence with dual climate variable in the 3D form scatter plot. Machine learning techniques such as clustering and regression is done to compare the sum square of residual (SSE) to conclude which climate variable is giving a big impact on dengue cases. Unsupervised techniques of K-means clustering is done to group the data accordingly. Averaged silhouette width method is used to define the number of K group. Each cluster the regression model is built and SSE is shown in table. Thus through the SSE the model validity can be known. © 2016 IEEE.
%Z cited By 2; Conference of 2016 IEEE Conference on Systems, Process and Control, ICSPC 2016 ; Conference Date: 16 December 2016 Through 18 December 2016; Conference Code:127664