Scientific data mining with StripMiner/sup TM/

The paper introduces scientific data mining, the standard data-mining problem, and the strip-mining problem. StripMiner/sup TM/, 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 StripMiner/sup TM/ code, its functionality, and its options.

[1]  Martin Fodslette Møller,et al.  A scaled conjugate gradient algorithm for fast supervised learning , 1993, Neural Networks.

[2]  Donald E. Brown,et al.  Fast generic selection of features for neural network classifiers , 1992, IEEE Trans. Neural Networks.

[3]  M. J. Embrechts,et al.  Neural network analysis of Doppler-broadened neutron absorption resonance data , 2001, SMCia/01. Proceedings of the 2001 IEEE Mountain Workshop on Soft Computing in Industrial Applications (Cat. No.01EX504).

[4]  C. Hansch,et al.  Comparative Quantitative Structure−Activity Relationship Studies on Anti-HIV Drugs , 1999 .

[5]  Robert H. Kewley,et al.  Data strip mining for the virtual design of pharmaceuticals with neural networks , 2000, IEEE Trans. Neural Networks Learn. Syst..

[6]  Claudio J. Meneses,et al.  Categorization And Evaluation Of Data MiningTechniques , 1970 .

[7]  Mark J. Embrechts,et al.  Exploring Financial Crises Data with Self-Organizing Maps (SOM) , 2001, WSOM.

[8]  Kristin P. Bennett,et al.  Feature selection for in-silico drug design using genetic algorithms and neural networks , 2001, SMCia/01. Proceedings of the 2001 IEEE Mountain Workshop on Soft Computing in Industrial Applications (Cat. No.01EX504).