CLUSTER ANALYSIS OF INDIVIDUAL PARTICLE ATMOSPHERIC DATA.

T. W. Shattuck, M. S. Germani, P R Buseck

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

K-means cluster analysis is an adequate method for reducing individual particle data, as long as extra clusters are used to allow for the non-spherical shape and natural variability of aerosol particles. The above method for choosing seedpoints does a particularly good job of detecting the types of low abundance particles that are interesting for urban atmospheric studies. The basic structure of the particle-type data increases the usefulness of factor rotation schemes. Application to the Phoenix aerosol suggests that the ability to discriminate between various types of crustal particles may yield valuable information in addition to that derived from particle types more commonly associated with anthropogenic activity.

Original languageEnglish (US)
Title of host publicationNational Meeting - American Chemical Society, Division of Environmental Chemistry
PublisherACS
Pages103-106
Number of pages4
Volume24
Edition2
StatePublished - 1984
Externally publishedYes

ASJC Scopus subject areas

  • Engineering(all)

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