Environmental chemistry through intelligent atmospheric data analysis

被引:42
|
作者
Gross, Deborah S. [1 ]
Atlas, Robert [2 ]
Rzeszotarski, Jeffrey [2 ]
Turetsky, Emma [2 ]
Christensen, Janara [2 ]
Benzaid, Sami [2 ]
Olson, Jamie [2 ]
Smith, Thomas [2 ]
Steinberg, Leah [2 ]
Sulman, Jon [2 ]
Ritz, Anna [2 ]
Anderson, Benjamin [2 ]
Nelson, Catherine [2 ]
Musicant, David R. [2 ]
Chen, Lei [3 ]
Snyder, David C. [4 ]
Schauer, James J. [4 ]
机构
[1] Carleton Coll, Dept Chem, Northfield, MN 55057 USA
[2] Carleton Coll, Dept Comp Sci, Northfield, MN 55057 USA
[3] Univ Wisconsin, Dept Comp Sci, Madison, WI 53705 USA
[4] Univ Wisconsin, Environm Chem & Technol Program, Madison, WI 53705 USA
关键词
Mass spectrometry; Clustering; Aerosol particle; Data mining; Database design; Data and knowledge visualization; User interfaces; LASER MASS-SPECTROMETRY; PARTICLE ANALYSIS; CLUSTER-ANALYSIS; CLASSIFICATION; ALGORITHMS; MANAGEMENT; SOFTWARE; SYSTEM; ART-2A;
D O I
10.1016/j.envsoft.2009.12.001
中图分类号
TP39 [计算机的应用];
学科分类号
081203 ; 0835 ;
摘要
Here we present a new open-source software package designed to facilitate the analysis of atmospheric data, with emphasis on data mining applications applied to single-particle mass spectrometry data from aerosol particles. The software package, Enchilada (Environmental Chemistry through Intelligent Atmospheric Data Analysis), is designed to seamlessly handle large datasets, to allow for temporal aggregation of data from many instruments, and to integrate techniques such as clustering (K-means, K-medians, and Art-2a), labeling of peaks in mass spectra, and temporal correlations of multiple datasets from multiple instrument types. The software, which continues to be developed and improved, provides users with a single package to integrate data from multiple mass spectrometer systems (ATOFMS, PALMS, SPASS, Q-AMS) as well as any time-based data stream. A detailed description of the software and examples of analysis methods that are incorporated into it are described here. (C) 2009 Elsevier Ltd. All rights reserved.
引用
收藏
页码:760 / 769
页数:10
相关论文
共 50 条
  • [41] Deep Learning And Data Mining Classification Through the Intelligent Agent Reasoning
    Chemchem, Amine
    Alin, Francois
    Krajecki, Michael
    2018 IEEE 6TH INTERNATIONAL CONFERENCE ON FUTURE INTERNET OF THINGS AND CLOUD WORKSHOPS (W-FICLOUD 2018), 2018, : 13 - 20
  • [42] Variational data assimilation of airborne sensing profiles to the transport and transformation model of atmospheric chemistry
    Penenko, Alexey
    Antokhin, Pavel
    Grishina, Anastasia
    23RD INTERNATIONAL SYMPOSIUM ON ATMOSPHERIC AND OCEAN OPTICS: ATMOSPHERIC PHYSICS, 2017, 10466
  • [43] Identifying user habits through data mining on call data records
    Bianchi, Filippo Maria
    Rizzi, Antonello
    Sadeghian, Alireza
    Moiso, Corrado
    ENGINEERING APPLICATIONS OF ARTIFICIAL INTELLIGENCE, 2016, 54 : 49 - 61
  • [44] Intelligent HAZOP analysis method based on data mining
    Wang, Feng
    Gu, Wunan
    JOURNAL OF LOSS PREVENTION IN THE PROCESS INDUSTRIES, 2022, 80
  • [45] The Application of Neural Networks for the Intelligent Analysis of Multidimensional Data
    Beley, Olexander
    Chaplyha, Volodymyr
    2017 4TH INTERNATIONAL SCIENTIFIC-PRACTICAL CONFERENCE PROBLEMS OF INFOCOMMUNICATIONS-SCIENCE AND TECHNOLOGY (PIC S&T), 2017, : 400 - 404
  • [46] Cluster Analysis of Three-Way Atmospheric Data
    Morlini, Isabella
    Orlandini, Stefano
    ADVANCES IN STATISTICAL MODELS FOR DATA ANALYSIS, 2015, : 177 - 189
  • [47] Environmental Data Science
    Gibert, Karina
    Horsburgh, Jeffery S.
    Athanasiadis, Ioannis N.
    Holmes, Geoff
    ENVIRONMENTAL MODELLING & SOFTWARE, 2018, 106 : 4 - 12
  • [48] An Efficiency Analysis on the TPA Clustering Methods for Intelligent Customer Segmentation
    Sheshasaayee, Ananthi
    Logeshwari, L.
    2017 INTERNATIONAL CONFERENCE ON INNOVATIVE MECHANISMS FOR INDUSTRY APPLICATIONS (ICIMIA), 2017, : 784 - 788
  • [49] Correction of Atmospheric Model Through Data Mining With Historical Data of Two-Line Element
    Bai, Xue
    Liao, Chuan
    Xu, Ming
    Zheng, Yaru
    IEEE ACCESS, 2020, 8 : 123272 - 123286
  • [50] Automated trend analysis of proteomics data using an intelligent data mining architecture
    Malone, J
    McGarry, K
    Bowerman, C
    EXPERT SYSTEMS WITH APPLICATIONS, 2006, 30 (01) : 24 - 33