Analyzing Climate Variability in Malaysia Using Association Rule Mining

Rabiatul A A Rashid,Puteri N E Nohuddin,Zuraini Zainol

doi:10.14419/ijet.v7i4.34.26881

Rabiatul A A Rashid, Puteri N E Nohuddin + Show 1 more

Open Access

https://doi.org/10.14419/ijet.v7i4.34.26881

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Abstract

Previous surveys proved that data mining is one of the methods that can be utilized for climate prediction, predominantly clustering and classification are the most applied methods in data mining to build a model to predict changes in the climate. Unlike the climate change, climate variability is a phenomenon where the occurrence of climate uncertainty is according to the changes year to year basis. This study is focusing to look at the effectiveness of the Association Rule Mining (ARM) techniques in predicting climate variability events in Malaysia. In this report, it explained how the patterns that exist within climate data is discovered using ARM and how the extracted pattern is used to predict climate variability. In this report also, a framework is developed to explain how ARM can generate rules and extract patterns from the data and how the extracted rules and patterns is used to develop a model for predicting climate variability event.

Highlights

Knowledge Discovery in Database consists of a few process and one of the main process is Data Mining [1] where it is a process aim to discover unknown information from the massive amount of data
Association Rule Mining (ARM) has been applied in many real world problems such as finding patterns in documents [9,10], predicting floods [11], trend analysis of social networks [12], monitoring elderly people [13], etc
The data will be divided into specific groups. It is different with ARM, where patterns and rules are obtained from relationship discovered within the database [19]

Summary

Introduction

Knowledge Discovery in Database consists of a few process and one of the main process is Data Mining [1] where it is a process aim to discover unknown information from the massive amount of data. The most commonly used techniques are ARM, classification and clustering These methods are commonly used in research in getting the most appropriate and accurate techniques for modeling climate forecasting. The data will be divided into specific groups It is different with ARM, where patterns and rules are obtained from relationship discovered within the database [19]. This method is considered as useful for climate prediction due to the fact that climate data consist of various elements and factors

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Conclusion