Majority Voting Based Multi-Task Clustering of Air Quality Monitoring Network in Turkey


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Tüysüzoğlu G., Birant D., Pala A.

APPLIED SCIENCES-BASEL, vol.9, no.8, 2019 (SCI-Expanded) identifier identifier

  • Publication Type: Article / Article
  • Volume: 9 Issue: 8
  • Publication Date: 2019
  • Doi Number: 10.3390/app9081610
  • Journal Name: APPLIED SCIENCES-BASEL
  • Journal Indexes: Science Citation Index Expanded (SCI-EXPANDED), Scopus
  • Keywords: air pollution, multi-task clustering, air quality, machine learning, data mining, PM10 CONCENTRATIONS, PREDICTION, MODEL
  • Dokuz Eylül University Affiliated: Yes

Abstract

Air pollution, which is the result of the urbanization brought by modern life, has a dramatic impact on the global scale as well as local and regional scales. Since air pollution has important effects on human health and other living things, the issue of air quality is of great importance all over the world. Accordingly, many studies based on classification, clustering and association rule mining applications for air pollution have been proposed in the field of data mining and machine learning to extract hidden knowledge from environmental parameters. One approach is to model a region in a way that cities having similar characteristics are determined and placed into the same clusters. Instead of using traditional clustering algorithms, a novel algorithm, named Majority Voting based Multi-Task Clustering (MV-MTC), is proposed and utilized to consider multiple air pollutants jointly. Experimental studies showed that the proposed method is superior to five well-known clustering algorithms: K-Means, Expectation Maximization, Canopy, Farthest First and Hierarchical clustering methods.