# Majority Voting Based Multi-Task Clustering of Air Quality Monitoring Network in Turkey
## 1 Introduction  
Air pollution is now recognized as an important problem all over the world. It can be referred as a mixture of multiple pollutants that vary in size and composition. Air pollutants (also referred to as \"criteria pollutants\") are commonly grouped as particulate matters such as PM\({}_{10}\) and PM\({}_{2.5}\), and ground-level pollutants such as ozone (O\({}_{3}\)), carbon monoxide (CO), sulfur dioxide (SO\({}_{2}\)), nitrogen oxides (NO) and nitrogen dioxide (NO\({}_{2}\)). It is known that air pollution has negative impacts on human health, range of vision, materials, and plant and animal health. Air pollutants trigger or worsen chronic diseases such as asthma, pneumonia, heart attack, bronchitis and other respiratory problems. Since particulate matters are very small and light, they tend to stay in the air longer than the heavier particles. This increases the likelihood of humans and animals inhaling these particles through respiration. Due to their small size, these particles can easily pass through the nose and throat and penetrate the lungs, and some may even enter the circulatory system. Smoke, a gas mixture of solid and liquid particles resulting from non-burned carbon materials such as solid fuels and fuel oil, is a variety of air pollution and has a reducing effect on range of visibility. Air pollution also has a destructive and disturbing effect on artistic and architectural structures. On plants, they can be lethal and prevent their growth. Thus, high concentrations of air pollutants can harm human health, adversely influence environment, and also cause property damage [1; 2; 3; 4]. Due to the seriousness of the issue, air pollution control policies require systematic monitoring and evaluation of air quality. The causes of air pollution should be investigated and necessary precautions should be taken in accordance with the findings. Therefore, it is very important to develop an appropriate tool to understand the air quality in an area. For this purpose, effective methods are continuously developed with new studies. In this context, this study aimed at examining the air quality monitoring stations in Turkey according to their similarities in terms of five air pollutants, PM\({}_{10}\), SO\({}_{2}\), NO\({}_{2}\), NO and O\({}_{3}\), and making appropriate inferences based on the analysis of the levels of air pollutants measured at these stations in the time interval from 1 November 2017 to 1 November 2018. In this way, city areas with similar air pollution behavior can be identified so that decision-making authority can canalize emission sources to be located to the regions in need. To perform the experiments, a novel algorithm, named majority-based multi-task clustering (MV-MTC), is proposed, instead of applying traditional clustering algorithms, to benefit from the common decision coming from different pollutant sources. The novelty of this study is the implementation of multi-task clustering (MTC) in the field of environmental science and the examination of air pollution in Turkey with this method for the first time. The proposed algorithm (MV-MTC) was compared with popular clustering algorithms, namely K-Means, Expectation Maximization, Canopy, Farthest First and Hierarchical clustering methods, in terms of sum of squared error (SSE). The experimental results obtained in this study indicate that the proposed approach produces better clusters than standard clustering algorithms by considering relationships among multiple air pollutants jointly. The remainder of this study is organized as follows. In Section 2, a detailed literature survey investigating the studies using data mining methods to deal with the air quality control of Turkey is given in addition to the recent studies on the proposed method of multi-task clustering. In Section 3, background information on the applied methodology used in the experiments is explained. The proposed MTC technique and dataset description are mentioned in Sections 4 and 5, respectively.