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Author(s): Hopke, P. | Prather, K.A. | Zhao, W.X. |
Year: 2008
Citation: ATMOSPHERIC ENVIRONMENT Volume: 42 Issue: 5 Pages: 881-892
Abstract: 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.
Topics:
Machine Learning,
Applications:
Chemical Analysis,
Models:
ART 2-A,
