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Data‐Mining Discovery of Pattern and Process in Ecological Systems
Author(s) -
HOCHACHKA WESLEY M.,
CARUANA RICH,
FINK DANIEL,
MUNSON ART,
RIEDEWALD MIREK,
SOROKINA DARIA,
KELLING STEVE
Publication year - 2007
Publication title -
the journal of wildlife management
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.94
H-Index - 111
eISSN - 1937-2817
pISSN - 0022-541X
DOI - 10.2193/2006-503
Subject(s) - data mining , exploratory data analysis , computer science , data science , parametric statistics , process (computing) , statistical model , statistical hypothesis testing , set (abstract data type) , variable (mathematics) , machine learning , statistics , mathematics , mathematical analysis , programming language , operating system
Most ecologists use statistical methods as their main analytical tools when analyzing data to identify relationships between a response and a set of predictors; thus, they treat all analyses as hypothesis tests or exercises in parameter estimation. However, little or no prior knowledge about a system can lead to creation of a statistical model or models that do not accurately describe major sources of variation in the response variable. We suggest that under such circumstances data mining is more appropriate for analysis. In this paper we 1) present the distinctions between data‐mining (usually exploratory) analyses and parametric statistical (confirmatory) analyses, 2) illustrate 3 strengths of data‐mining tools for generating hypotheses from data, and 3) suggest useful ways in which data mining and statistical analyses can be integrated into a thorough analysis of data to facilitate rapid creation of accurate models and to guide further research.