|کد مقاله||کد نشریه||سال انتشار||مقاله انگلیسی||ترجمه فارسی||نسخه تمام متن|
|4443173||1311181||2008||12 صفحه PDF||سفارش دهید||دانلود رایگان|
Cluster analysis of aerosol time-of-flight mass spectrometry (ATOFMS) data has been an effective tool for the identification of possible sources of ambient aerosols. In this study, the clustering results of two typical methods, adaptive resonance theory-based neural networks-2a (ART-2a) and density-based clustering of application with noise (DBSCAN), on ATOFMS data were investigated by employing a set of benchmark ATOFMS data. The advantages and disadvantages of these two methods are discussed and some feasible remedies proposed for problems encountered in the clustering process. The results of this study will provide promising directions for future work on ambient aerosol cluster analysis, suggesting a more effective and feasible clustering strategy based on the integration of ART-2a and DBSCAN.
Journal: Atmospheric Environment - Volume 42, Issue 5, February 2008, Pages 881–892