| Summary: | Air quality is one of the most popular environmental problems in this globalization era. Air
pollution is the poisonous air that comes from car emissions, smog, open burning, chemicals
from factories and other particles and gases. This harmful air can give adverse effects to
human health and the environment. In order to provide information which areas are better for
the residents in Malaysia, cluster analysis is used to determine the areas that can be clustering
together based on their air quality through several air quality substances. Monthly data from
37 monitoring stations in Peninsular Malaysia from the year 2013 to 2015 were used in this
study. K-Means (KM) clustering algorithm, Expectation Maximization (EM) clustering
algorithm and Density Based (DB) clustering algorithm have been chosen as the techniques to
analyze the cluster analysis by utilizing the Waikato Environment for Knowledge Analysis
(WEKA) tools. Results show that K-means clustering algorithm is the best method among other
algorithms due to its simplicity and time taken to build the model. The output of K-means
clustering algorithm shows that it can cluster the area into two clusters, namely as cluster 0
and cluster 1. Clusters 0 consist of 16 monitoring stations and cluster 1 consists of 36
monitoring stations in Peninsular Malaysia.
|
|---|
