# Majority Voting Based Multi-Task Clustering of Air Quality Monitoring Network in Turkey
## 2 Related Work  
The monitoring stations located in nearby area are characterized by the same specific air pollution characteristics. Many studies have been done in the literature using this information. Data mining and machine learning are intensely applied to environmental subjects to identify interesting structure in large amount of environmental data, where the structure finds patterns, rules, predictive models and relationships among the data. [PERSON], [PERSON] and [PERSON] [5] classified the air quality monitoring network in Piemonte (Northern Italy) using functional cluster analysis based on Partitioning around Medoids algorithm and considering three air pollutants, namely NO\({}_{2}\), PM\({}_{10}\), and O\({}_{3}\), to classify sites in homogeneous clusters and identify the representative ones. [PERSON], [PERSON], [PERSON] and [PERSON] [6] analyzed and made experiments on the variations of PM\({}_{10}\) concentrations at 43 stations in the air quality monitoring network of the Basque Country to group them according to their common characteristics. They implemented the autocorrelation function and K-means clustering. Similarly, [PERSON], [PERSON] and [PERSON] [7] used principal component analysis and cluster analysis for the management of air quality monitoring network of Hong Kong and for the reduction of associated expenses. In Turkey, the importance of environmental issues has also gained much attention and studies related to air quality increasingly continue in this direction. Several of environmental data mining studies mentioned thus far on \"air quality in Turkey\" are compared in Table 1 by displaying the year of the publication, the target pollutants of the study, dataset content used in the experiments, the aim of the study, which data mining task was applied and which algorithms/methods were implemented as well as performance metrics to evaluate the results of the applied methodology. The bold notation in the Algorithms/Methods column shows the algorithm which performs the best among the others. According to the findings, most of the experiments are done using the measurementsof PM\({}_{10}\) concentrations [8; 9; 10; 11; 12; 13] and prediction of air pollutant amount is the main goal. In addition to pollution data, some of the studies also integrate meteorological data such as temperature, wind speed, wind direction, pressure and humidity into the problem domain [8; 11; 12; 13]. Multi-task learning (MTL) is a learning technique in which useful information contained in a number of relevant tasks is leveraged to help improve the overall performance of all tasks. All of these tasks or at least a subset of them are assumed to be related to each other. In many of the applications, it is found that learning these tasks jointly leads to performance improvement compared with learning them individually. In most fields comprising computer vision, bioinformatics, health informatics, speech and natural language processing, web applications and ubiquitous computing, MTL is used to enhance the overall performance of the applications involved. Learning paradigms including supervised learning (e.g., classification or regression problems) [14; 15; 16], unsupervised learning [17; 18; 19; 20; 21; 22; 23], semi-supervised learning [24; 25; 26; 27], active learning [28; 29; 30; 31], reinforcement learning [32; 33; 34; 35], multi-view learning [36; 21; 37; 38], and graphical models [39; 40; 41] are generally combined with MTL [42; 43]. _Multi-task classification_ and _multi-task clustering_ are two well-known types of multi-task learning recently presented in the literature. [PERSON], [PERSON], [PERSON], [PERSON] and [PERSON] [44] use multi-task classification in the prediction of air pollution particles by implementing a deep multi-task learning framework. On the other hand, multi-task clustering has not been studied until now for the air quality management, neither in the environmental science. There is an issue to be addressed: \"what to share\" while learning multiple tasks.