An agglomerative hierarchical clustering with association rules for discovering climate change patterns

Abstract

Ozone analysis is the process of identifying meaningful patterns that would facilitate the prediction of future trends. One of the common techniques that have been used for ozone analysis is the clustering technique. Clustering is one of the popular methods which contribute a significant knowledge for time series data mining by aggregating similar data in specific groups. However, identifying significant patterns regarding the ground-level ozone is quite a challenging task especially after applying the clustering task. This paper presents a pattern discovery for ground-level ozone using a proposed method known as an Agglomerative Hierarchical Clustering with Dynamic Time Warping (DTW) as a distance measure on which the patterns have been extracted using the Apriori Association Rules (AAR) algorithm. The experiment is conducted on a Malaysian Ozone dataset collected from Putrajaya for year 2006. The experiment result shows 20 pattern influences on high ozone with a high confident (1.00). However, it can be classified into four meaningful patterns; more high temperature with low nitrogen oxide, nitrogen oxide and nitrogen dioxide high, nitrogen oxide with carbon oxide high, and carbon oxide high. These patterns help in decision making to plan the amount of carbon oxide and nitrogen oxide to be reduced in order to avoid the high ozone surface.