Nonmetric clustering: new approaches for ecological data

Author

Matthews, Geoffrey ; Matthews, Robin ; Landis, Wayne

Author_Institution

Western Washington Univ., Bellingham, WA, USA

fYear

1994

fDate

1-4 Mar 1994

Firstpage

482

Lastpage

483

Abstract

Ecological studies and multispecies ecotoxicological tests are based on the examination of a variety of physical, chemical and biological data with the intent of finding patterns in their changing relationships over time. The data sets resulting from such studies are often noisy, incomplete, and difficult to envision. We have developed machine learning and visualization software to aid in the analysis, modelling, and understanding of such systems. The software is based on nonmetric conceptual clustering, which attempts to analyze the data into clusters that are strongly associated with several measured parameters. Our analysis and visualization tools not only confirmed suspected ecological patterns, but revealed aspects of the data that were unnoticed by ecologists using conventional statistical techniques

Keywords

biology computing; data analysis; data visualisation; ecology; learning (artificial intelligence); pattern recognition; biological data; changing relationships; chemical data; ecological data; incomplete data; machine learning; measured parameters; multispecies ecotoxicological tests; noisy data sets; nonmetric conceptual clustering; pattern finding; physical data; statistical techniques; system modelling; visualization software; Biological system modeling; Chemicals; Clustering algorithms; Data analysis; Data visualization; Machine learning; Software measurement; Software systems; Termination of employment; Testing;

fLanguage

English

Publisher

ieee

Conference_Titel

Artificial Intelligence for Applications, 1994., Proceedings of the Tenth Conference on

Conference_Location

San Antonia, TX

Print_ISBN

0-8186-5550-X

Type

conf

DOI

10.1109/CAIA.1994.323629

Filename

323629