TY  - BOOK
AU  - Xanthopoulos,Petros
AU  - Pardalos,Panos M.
AU  - Trafalis,Theodore B.
ED  - SpringerLink (Online service)
TI  - Robust Data Mining
T2  - SpringerBriefs in Optimization,
SN  - 9781441998781
AV  - QA402.5-402.6 
U1  - 519.6 23
PY  - 2013///
CY  - New York, NY
PB  - Springer New York, Imprint: Springer
KW  - Mathematics
KW  - Software engineering
KW  - Data mining
KW  - Mathematical optimization
KW  - Optimization
KW  - Data Mining and Knowledge Discovery
KW  - Software Engineering/Programming and Operating Systems
N1  - 1. Introduction -- 2. Least Squares Problems -- 3. Principal Component Analysis -- 4. Linear Discriminant Analysis -- 5. Support Vector Machines -- 6. Conclusion
N2  - Data uncertainty is a concept closely related with most real life applications that involve data collection and interpretation. Examples can be found in data acquired with biomedical instruments or other experimental techniques. Integration of robust optimization in the existing data mining techniques aim to create new algorithms resilient to error and noise. This work encapsulates all the latest applications of robust optimization in data mining. This brief contains an overview of the rapidly growing field of robust data mining research field and presents  the most well known machine learning algorithms, their robust counterpart formulations and algorithms for attacking these problems. This brief will appeal to theoreticians and data miners working in this field
UR  - http://dx.doi.org/10.1007/978-1-4419-9878-1
ER  -