Title :
Scientific data mining with StripMinerTM
Author :
Embrechts, Mark J. ; Arciniegas, Fabio ; Ozdemir, M. ; Momma, Michinari
Author_Institution :
Dept. of Decision Sci. & Eng. Syst., Rensselaer Polytech. Inst., Troy, NY, USA
Abstract :
The paper introduces scientific data mining, the standard data-mining problem, and the strip-mining problem. StripMinerTM , a shell program for feature reduction and predictive modeling, integrates the executions of several different machine-learning models (partial least squares regression, genetic algorithms, support vector machines, neural networks, and local learning). This paper introduces the StripMinerTM code, its functionality, and its options
Keywords :
data mining; genetic algorithms; learning (artificial intelligence); learning automata; least squares approximations; neural nets; scientific information systems; StripMiner; feature reduction; genetic algorithms; local learning; machine learning models; neural networks; partial least squares regression; predictive modeling; scientific data mining; shell program; strip mining problem; support vector machines; Application software; Data engineering; Data mining; Genetic algorithms; Least squares methods; Machine learning; Neural networks; Predictive models; Support vector machines; Systems engineering and theory;
Conference_Titel :
Soft Computing in Industrial Applications, 2001. SMCia/01. Proceedings of the 2001 IEEE Mountain Workshop on
Conference_Location :
Blacksburg, VA
Print_ISBN :
0-7803-7154-2
DOI :
10.1109/SMCIA.2001.936721
