A general-purpose program for multivariate data analysis

[1]  Olav M. Kvalheim,et al.  Latent-structure decompositions (projections) of multivariate data , 1987 .

[2]  J. Bezdek,et al.  FCM: The fuzzy c-means clustering algorithm , 1984 .

[3]  O. Kvalheim,et al.  Grouping of bacteria by simca pattern recognition on gas chromatographic lipid data: Patterns among Moraxella and rod-shaped Neisseria , 1987 .

[4]  Terje V. Karstang,et al.  Multivariate calibration of an x-ray diffractometer by partial least squares regression , 1987 .

[5]  Multivariate calibration : quantitative interpretation of non-selective chemical data , 1985 .

[6]  S. Wold,et al.  The Collinearity Problem in Linear Regression. The Partial Least Squares (PLS) Approach to Generalized Inverses , 1984 .

[7]  Terje V. Karstang,et al.  Multivariate calibration of diffuse reflectance infrared spectra of coals as an alternative to rank determination by vitrinite reflectance , 1987 .

[8]  Erik Johansson,et al.  Four levels of pattern recognition , 1978 .

[9]  B. Kowalski,et al.  Pattern recognition. Powerful approach to interpreting chemical data , 1972 .

[10]  D. Aksnes,et al.  Crude oil characterization and correlation by principal component analysis of carbon-13 nuclear magnetic resonance spectra , 1985 .

[11]  K. Sjoedin.  Minimizing effects of closure on analytical data , 1984 .

[12]  O. Kvalheim,et al.  Visualizing information in multivariate data: Applications to petroleum geochemistry : Part 2. Interpretation and correlation of north sea oils by using three different biomarker fractions , 1986 .

[13]  Svante Wold,et al.  Pattern recognition by means of disjoint principal components models , 1976, Pattern Recognit..