# Majority Voting Based Multi-Task Clustering of Air Quality Monitoring Network in Turkey
## 4 Multi-task Clustering
* Multi-task clustering can also be used as a preprocessing step to improve the speed and performance of the classification algorithm that is used to predict air quality index. * In the application to predict air quality index, temporal data clustering results can give information about air quality variations, such that a set of forecasting systems, which are dedicated to reflect temporal changes, can be formed. * The identification of the air pollution levels of the different regions by clustering can be useful to design air quality monitoring network structure. Such networks must consider the monitoring location, sampling frequencies and the pollutants concern. For instance, clustering results lead to design an optimal network, i.e., a network providing maximum data with minimum measurement devices. The spatial relationship analysis is used to compare the information given by the potential sites that may form the network. * On forecasting the level of air pollution, it is possible to find the closest cluster of a new instance to be predicted, and then use the values in this cluster for prediction. * Multi-task clustering can also be useful for detecting the extreme air pollution events and can help predict future exceedances. In this sense, an air pollutant value of a region may be considered as an outlier if it exceeds the minimum or maximum value of the cluster it belongs to. \begin{table}
\begin{tabular}{c c c c c c c c c c c} \hline \hline  & **KM** & **KM++** & **EM** & **Canopy** & **FFist** & **HIER\({}_{\text{Sing}}\)** & **HIER\({}_{\text{Comp}}\)** & **HIER\({}_{\text{Avg}}\)** & **HIER\({}_{\text{Moon}}\)** & **HIER\({}_{\text{Centro}}\)** \\ \hline C\({}_{\text{PMOD}}\) & 0.04 & 0.05 & 0.32 & 0.03 & 0.03 & 0.11 & 0.11 & 0.11 & 0.11 & 0.16 \\ C\({}_{\text{CQ2}}\) & 0.04 & 0.03 & 0.36 & 0.04 & 0.03 & 0.11 & 0.11 & 0.11 & 0.11 & 0.17 \\ C\({}_{\text{XQ2}}\) & 0.03 & 0.04 & 0.29 & 0.03 & 0.03 & 0.11 & 0.11 & 0.11 & 0.11 & 0.16 \\ C\({}_{\text{ND}}\) & 0.04 & 0.04 & 0.26 & 0.03 & 0.03 & 0.11 & 0.11 & 0.11 & 0.11 & 0.17 \\ C\({}_{\text{CQ3}}\) & 0.05 & 0.04 & 0.30 & 0.03 & 0.11 & 0.11 & 0.11 & 0.12 & 0.16 \\ C\({}_{\text{ALL}}\) & 0.20 & 0.20 & 1.53 & 0.16 & 0.15 & 0.55 & 0.55 & 0.56 & 0.82 \\ MV-MTC & 0.31 & 0.60 & 1.55 & 0.33 & 0.29 & 0.73 & 0.73 & 0.73 & 0.76 & 1.05 \\ \hline \hline \end{tabular}
\end{table} 
Table 10: Comparisons of different clustering methods in terms of execution time (in seconds). ## 7 Conclusions

The main purpose of this study was to present a new multi-task clustering algorithm to determine which provinces of Turkey have the same air pollution characteristics so that similar precautions for the reduction of pollution can be taken by the decision-making authority for the cities in the same group. The main air pollutants for the experiments were selected as PM\({}_{10}\), SO\({}_{2}\), NO\({}_{2}\), NO and O\({}_{3}\) and their mean daily concentrations were taken into consideration. All of the data were taken from 49 air quality monitoring stations from different regions of Turkey. Two phases were performed under MV-MTC scheme: single-task clustering and multi-task clustering. In single-task clustering, each air pollutant was handled individually and air quality monitoring stations were assigned to respective clusters (_local clustering_). In multi-task clustering phase, clusters were labeled according to the intra-cluster weights so that taking common decision from different tasks becomes easier by applying majority voting on these cluster labels per each instance. Final cluster labels were obtained in this phase by combining the results of single-task clusters (_global clustering_). According to the results of the sum of squared error, the proposed multi-task clustering method MV-MTC performed well compared to classical single clustering algorithms K-Means, Expectation Maximization, Canopy, Farthest First and Hierarchical clustering. MV-MTC with K-Means, which was initialized with K-Means++, provides promising results in the detection of similar AQMSs.