The DOI matches its Crossref record; 175 of the 175 references it cites were found in a registry or an index.
Cluster and Classification Techniques for the Biosciences
Abstract
Advances in experimental methods have resulted in the generation of enormous volumes of data across the life sciences. Hence clustering and classification techniques that were once predominantly the domain of ecologists are now being used more widely. This 2006 book provides an overview of these important data analysis methods, from long-established statistical methods to more recent machine learning techniques. It aims to provide a framework that will enable the reader to recognise the assumptions and constraints that are implicit in all such techniques. Important generic issues are discussed first and then the major families of algorithms are described. Throughout the focus is on explanation and understanding and readers are directed to other resources that provide additional mathematical rigour when it is required. Examples taken from across the whole of biology, including bioinformatics, are provided throughout the book to illustrate the key concepts and each technique's potential.
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A framework for delineating biogeographical regions based on species distributions
Kreft, H., & Jetz, W. (2010) · Journal of Biogeography · doi:10.1111/j.1365-2699.2010.02375.x · Cited by 743
Traffic accident detection using random forest classifier
Dogru, N., & Subasi, A. (2018) · 2018 15th Learning and Technology Conference (L&T) · doi:10.1109/lt.2018.8368509 · Cited by 308
Prevalence, thresholds and the performance of presence–absence models
Lawson, C. R., Hodgson, J. A., Wilson, R. J., & Richards, S. A. (2014) · Methods in Ecology and Evolution · doi:10.1111/2041-210x.12123 · Cited by 194
Metadata
- Authors
- Fielding, A. H.
- Venue
- Cambridge University Press eBooks
- Publisher
- Cambridge University Press
- Published
- 2006-12-14
- OpenAlex
- W1484358094
- Cited by
- 266
- References
- 175
- FWCI
- 3.10
- Type
- book
- Language
- en
- Open access
- closed
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