@article{article_270017, title={Time Series Clustering’s application to identifying of information redundancy at air pollution monitoring stations in Turkey}, journal={Sakarya University Journal of Science}, volume={20}, pages={605–616}, year={2016}, DOI={10.16984/saufenbilder.69439}, author={İşçi Güneri, Öznur and Güler Dinçer, Nevin and Yalçın, Muhammet Oğuzhan}, keywords={Autoregressive model,fuzzy k-medoids clustering,air quality monitoring stations}, abstract={<p class="6zetAbstractMetin">The aim of study is to determine the monitoring stations having similar behavior with respect to PM <sub>10 </sub> and SO <sub>2 </sub> concentrations and thus decrease monitoring cost and information redundancy. For this purpose, autoregressive model based Fuzzy K-medoids algorithm is used. At the results of analyses, it has been concluded that information redundancy in monitoring stations and thus monitoring cost can be decreased approximately 78.5% for PM <sub>10 </sub> air pollutant, 73.5% for SO <sub>2 </sub> air pollutant.   </p>}, number={3}, publisher={Sakarya University}