Abstract: This article provides a general overview of how portfolio is used in education and then goes on to discuss the development of a generic, institution-wide portfolio for students. We further provide a succinct summary and critical analysis of the educational principles underlying the use of portfolio in higher education. This is followed by an overview of the growing number of portfolio initiatives currently underway at the Vrije Universiteit Brussel. As the use of portfolio is a natural complement to competence-oriented educational innovations, the Educational Innovation and Educational Service Centre (OSC) of the Vrije Universiteit Brussel are developing a generic electronic portfolio system. The functional and technical requirements for implementation of eportfolio have been established on the basis of the lessons learned from practical experience. The choice of an open source development environment made it possible to get a prototype up and running relatively rapidly, thanks to the availability of built-in tools such as user management, security management, content management and plug-ins. The open character of the system offers the best guarantees for its future development, flexibility and the possibility of linking it to other projects and databanks. The article concludes with a description of the portfolio system at its present stage of development together with an exploration of the future possibilities of eportfolio

Abstract: Feature selection is a valuable technique in data analysis for information-preserving data reduction. This paper describes Classification and Regression Trees (CART) and Multivariate Regression Trees (MRT)-based approaches for both supervised and unsupervised feature selection. The well-known CART method allows to perform supervised feature selection by modeling one response variable (y) by some explanatory variables (x). The recently proposed CART extension, MRT can handle more than one response variable (y). This allows to perform a supervised feature selection in the presence of more than one response variable. For unsupervised feature selection, where no response variables are available, we propose Auto-Associative Multivariate Regression Trees (AAMRT) where the original variables (x) are not only used as explanatory variables (x), but also as response variables (y=x). Since (AA)MRT is grouping the objects into groups with similar response values by using explanatory variables, this means that the variables are found which are most responsible for the cluster structure in the data. We will demonstrate how these approaches can improve (the detection of) the cluster structure in data and bow they can be used for knowledge discovery. (c) 2004 Elsevier B.V All rights reserved.

Abstract: The present paper deals with the application of different chemometric methods to an environmental data set derived from the monitoring of wet precipitation in Austria (1988-1999). These methods are: principal component analysis (PCA); projection pursuit (PP); density-based spatial clustering of application with noise (DBSCAN); ordering points to identify the clustering structures (OPTICS); self-organizing maps (SOM), also called the Kohonen network; and the neural gas (NG) network. The aim of the study is to introduce some new approaches into environmetrics and to compare their usefulness with already existing techniques for the classification and interpretation of environmental data. The density-based approaches give information about the occurrence of natural clusters in the studied data set, which, however, do not occur in the case presented here; information about high-density zones (very similar samples) and extreme samples is also obtained. The partitioning techniques (clustering, but also neural gas and Kohonen networks) offer an opportunity to classify the objects of interest into several defined groups, the patterns of ionic concentration of which can be studied in detail. The visual aids, such as the color map and the Kohonen map, for each site are very helpful in understanding the relationships between samples and between samples and variables. All methods, and in particular projection pursuit, give information about samples with extreme characteristics. (c) 2004 Elsevier Ltd. All rights reserved.

Abstract: The present paper deals with the application of different chemometric methods to an environmental data set derived from the monitoring of wet precipitation in Austria (1988-1999). These methods are: principal component analysis (PCA); projection pursuit (PP); density-based spatial clustering of application with noise (DBSCAN); ordering points to identify the clustering structures (OPTICS); self-organizing maps (SOM), also called the Kohonen network; and the neural gas (NG) network. The aim of the study is to introduce some new approaches into environmetrics and to compare their usefulness with already existing techniques for the classification and interpretation of environmental data. The density-based approaches give information about the occurrence of natural clusters in the studied data set, which, however, do not occur in the case presented here; information about high-density zones (very similar samples) and extreme samples is also obtained. The partitioning techniques (clustering, but also neural gas and Kohonen networks) offer an opportunity to classify the objects of interest into several defined groups, the patterns of ionic concentration of which can be studied in detail. The visual aids, such as the color map and the Kohonen map, for each site are very helpful in understanding the relationships between samples and between samples and variables. All methods, and in particular projection pursuit, give information about samples with extreme characteristics.

Abstract: To define starting conditions for the development of methods to separate impurities from the active substance and from each other in drugs with an unknown impurity profile, the parallel application of generic orthogonal chromatographic systems could be useful. The possibilities to define orthogonal chromatographic systems were examined by calculation of the correlation coefficients between retention factors k for a set of 68 drugs on 11 systems, by visual evaluation of the selectivity differences, by using principal component analysis, by drawing color maps and evaluating dendrograms. A zirconia-based stationary phase coated with a polybutadiene (PBD) polymer and three silica-based phases (base-deactivated, polar-embedded and monolithic) were used. Besides the stationary phase, the influence of pH and of organic modifier, on the selectivity of a system were evaluated. The dendrograms of hierarchical clusters were found good aids to assess orthogonality of chromatographic systems. The PBD-zirconia phase/methanol/pH 2.5 system is found most orthogonal towards several silica-based systems, e.g. a base-deactivated C-16-amide silica/methanol/pH 2.5 system. The orthogonality was validated using cross-validation, and two other validation sets, i.e. a set of non-ionizable solutes and a mixture of a drug and its impurities. (C) 2002 Elsevier Science B.V. All rights reserved.

Abstract: The use of the classification and regression tree (CART) methodology was studied in a quantitative structure-retention relationship (QSRR) context on a data set consisting of the retentions of 83 structurally diverse drugs on a Unisphere PBD column, using isocratic elutions at pH 11.7. The response (dependent variable) in the tree models consisted of the predicted retention factor (log k(w)) of the solutes, while a set of 266 molecular descriptors was used as explanatory variables in the tree building. Molecular descriptors related to the hydrophobicity (log P and Hy) and the size (TPC) of the molecules were selected out of these 266 descriptors in order to describe and predict retention. Besides the above mentioned, CART was also able to select hydrogen-bonding and molecular complexity descriptors. Since these variables are expected from QSRR knowledge, it demonstrates the potential of CART as a methodology to understand retention in chromatographic systems. The potential of CART to predict retention and thus occasionally to select an appropriate system for a given mixture was also evaluated. Reasonably good prediction, i.e. only 9% serious misclassification, was observed. Moreover, some of the misclassifications probably are inherent to the data set applied. (C) 2003 Elsevier Science B.V. All rights reserved.

Abstract: The use of the classification and regression tree (CART) methodology was studied in a quantitative structure-retention relationship (QSRR) context on a data set consisting of the retentions of 83 structurally diverse drugs on a Unisphere PBD column, using isocratic elutions at pH 11.7. The response (dependent variable) in the tree models consisted of the predicted rention factor (log kw) of the solutes, while a set of 266 molecular descriptors was used as explanatory variables in the tree building. Molecular descriptors related to the hydrophobicity (log P and Hy) and the size (TPC) of the molecules were selected out of these 266 descriptors in order to describe and predict retention. Besides the above mentioned, CART was also able to select hydrogen-bonding and molecular complexity descriptors. Since these variables are expected from QSRR knowledge, it demonstrates the potential of CART as a methodology to understand retention in chromatographic systems. The potential of CART to predict retention and thus occasionally to select an appropriate system for a given mixture was also evaluated. Reasonably good prediction, i.e. only 9% serious misclassification, was observed. Moreover, some of the misclassifications probably are inherent to the data set applied.

Abstract: To define starting conditions for the development of methods to separate impurities from the active substance and from each other in drugs with an unknown impurity profile, the parallel application of generic orthogonal chromatographic systems could be useful. The possibilities to define orthogonal chromatographic systems were examined by calculation of the correlation coefficients between retention factors k for a set of 68 drugs on 11 systems, by visual evaluation of the selectivity differences, by using principal component analysis, by drawing color maps and evaluating dendrograms. A zirconia-based stationary phase coated with a polybutadiene (PBD) polymer and three silica-based phases (base-deactivated, polar-embedded and monolithic) were used. Besides the stationary phase, the influence of pH and of organic modifier, on the selectivity of a system were evaluated. The dendrograms of hierarchical clusters were found good aids to assess orthogonality of chromatographic systems. The PBD-zirconia phase/methanol/pH 2.5 system is found most orthogonal towards several silica-based systems, e.g. a base-deactivated C16 -amide silica/methanol/pH 2.5 system. The orthogonality was validated using cross-validation, and two other validation sets, i.e. a set of non-ionizable solutes and a mixture of a drug and its impurities.

Abstract: This article introduces the Neural-Gas network, a fast neural net-based method for clustering, and shows how it is applied to gas chromatographic patterns of Maillard reaction products. The advantages of Neural-Gas are compared to the K-means clustering method and one of the best-known neural methods for clustering, the Kohonen self-organising maps. Some novel combinations with visualization techniques are also presented. (C) 2002 Elsevier Science B.V. All rights reserved.

Abstract: Feature selection is a valuable technique in data analysis for information-preserving data reduction. This paper describes a feature selection approach for hierarchical clustering based on genetic algorithms using a fitness function that tries to minimize the difference between the dissimilarity matrix of the original feature set and the one of the reduced feature sets. Clustering trees based on reduced feature sets are comparable with those based on the complete feature set. Special measures to favor small reduced feature sets are discussed. (C) 2002 Elsevier Science B.V. All rights reserved.

Abstract: In the evaluation of large or complex data sets the use of visualization methods can be of great benefit. In this paper, the use of parallel co-ordinate geometry (PCG) plots, principal component analysis (PCA) and N-way PCA as visualization procedures for large multi-response experimental designs was compared with the more traditional approach of calculating factor effects by multiple linear regression. PCG plots are a recent addition to the visualization tools and offer a possibility to visualize multi-dimensional data in two dimensions while no calculations are required. It was found that PCA and PCG each have their own benefits and disadvantages. Both methods can be used to some extent to select optimal conditions. Moreover, it was possible to use the PCA score plot as a Pareto optimality plot that allowed to select the experiments considered to be Pareto optimal. Therefore, the examined visualization methods can be useful and complementary aids in the interpretation of large multi-response experimental design data and they add a multivariate dimension to the more classical univariate analysis of such data. (C) 2002 Elsevier Science B.V. All rights reserved.

Abstract: Feature selection is a valuable technique in data analysis for information-preserving data reduction. This paper describes the use of rough set theory (RST) to construct reducts in a supervised way for reducing the number of features in unsupervised clustering. The application to a hierarchical clustering of Pseudomonas species is presented as an example. The Wallace measure is used for the comparison of the clustering results based on the original data set and those based on the reduced data set. (C) 2002 Elsevier Science B.V All rights reserved.

Abstract: FT-IR spectra have been investigated for their ability to distinguish compounds which are chemically diverse and to produce clusters of compounds which makes sense chemically. Principal component analysis (PCA) was applied to the analysis of a small database of FT-IR spectra. The effect of the data pretreatment step of log transformation on spectral data pattern was also visualized by using PCA plots. The method of sequential projection pursuit (SPP) was applied to detect inhomogeneities in the data. Finally, cluster analysis of these spectra, depending on unweighted pair-group average linkage, was carried out. (C) 2001 Elsevier Science B.V. All rights reserved.

Abstract: FT-IR spectra have been investigated for their ability to distinguish compounds which are chemically diverse and to produce clusters of compounds which makes sense chemically. Principal component analysis (PCA) was applied to the analysis of a small database of FT-IR spectra. The effect of the data pretreatment step of log transformation on spectral data pattern was also visualized by using PCA plots. The method of sequential projection pursuit (SPP) was applied to detect inhomogeneities in the data. Finally, cluster analysis of these spectra, depending on unweighted pair-group average linkage, was carried out.

Abstract: The quality of a clustering of chemical data is determined by a proper choice of distance measures and data transformations. The latter aspect is often neglected and its importance is shown here. It is also shown that the V-shaped data structure that is often obtained in a principal component analysis of chemical data may indicate that the clustering of the raw data can lead to classifications that are not relevant from a chemical point of view and that the log double centering transform should be considered as a possible alternative. (C) 2001 Published by Elsevier Science B.V.

Abstract: A chemometric study has been conducted on a published data set consisting of the retention times of 83 substances, from five pharmacological families, on eight HPLC systems. Principal component analysis, clustering and sequential projection pursuit were applied. In this way it was investigated to what extent the combination of chromatography and chemometrics allows one to make conclusions about pharmacological activities of (candidate) drugs and what the contribution is of the different HPLC systems considered. (C) 2000 Elsevier Science BN. All rights reserved.

Abstract: Sequential projection pursuit (SPP) is proposed to detect inhomogeneities (clusters) in high-dimensional analytical data. Such inhomogeneities indicate that there are groups of objects (samples) with different chemical characteristics. The method is compared with principal component analysis (PCA), PCA is generally applied to visually explore structure in high-dimensional data, but is not specifically used to find clustering tendency. Projection pursuit (PP) is specifically designed to find inhomogeneities, but the original method is computationally very intensive. SPP combines the advantages of both methods and overcomes most of their weak points. In this method, latent variables are obtained sequentially according to their importance measured by the entropy index. This involves an optimization step, which is achieved by using a genetic algorithm. The performance of the method is demonstrated and evaluated, first on simulated data sets, and then on near-infrared and gas chromatography data sets. It is shown that SPP indeed reveals more easily information about inhomogeneities than PCA.

Abstract: An evaluation whether mass spectral data contain useful information for assessing similarity/diversity of drug compounds is presented. A comparative study was carried out between Ward's hierarchical agglomerative clustering, based on the 2D Daylight fingerprints or on the mass spectra, of a small database of 66 synthetic substances. The influence of normalization of the mass spectral data on the clustering result has also been studied. The results were subsequently compared with an expert's classification of the same small dataset, based on own evaluation according to known structure and pharmacological activity. (C) 2000 Elsevier Science B.V. All rights reserved.

Abstract: Sequential projection pursuit (SPP) is proposed to detect inhomogeneities (clusters) in high-dimensional analytical data. Such inhomogeneities indicate that there are groups of objects (samples) with different chemical characteristics. The method is compared with principal component analysis (PCA). PCA is generally applied to visually explore structure in high-dimensional data, but is not specifically used to find clustering tendency. Projection pursuit (PP) is specifically designed to find inhomogeneities, but the original method is computationally very intensive. SPP combines the advantages of both methods and overcomes most of their weak points. In this method, latent variables are obtained sequentially according to their importance measured by the entropy index. This involves an optimization step, which is achieved by using a genetic algorithm. The performance of the method is demonstrated and evaluated, first on simulated data sets, and then on near-infrared and gas chromatography data sets. It is shown that SPP indeed reveals more easily information about inhomogeneities than PCA.

Abstract: A chemometric study has been conducted on a published data set consisting of the retention times of 83 substances, from five pharmacological families, on eight HPLC systems. Principal component analysis, clustering and sequential projection pursuit were applied. In this way it was investigated to what extent the combination of chromatography and chemometrics allows one to make conclusions about pharmacological activities of (candidate) drugs and what the contribution is of the different HPLC systems considered.

Abstract: An evaluation whether mass spectral data contain useful information for assessing similarity/diversity of drug compounds is presented. A comparative study was carried out between Ward's hierarchical agglomerative clustering, based on the 2D Daylight fingerprints or on the mass spectra, of a small database of 66 synthetic substances. The influence of normalization of the mass spectral data on the clustering result has also been studied. The results were subsequently compared with an expert's classification of the same small dataset, based on own evaluation according to known structure and pharmacological activity.

Abstract: A computer program is described for the experimental set-up and interpretation of ruggedness tests. The implemented strategy was based on a number of case studies and contains both recommended designs and minimal designs. The minimal designs reduce the number of experiments, but they cannot be statistically interpreted based on the interaction or dummy factor effects. The use of randomization tests as an alternative statistical interpretation method for the significance of the effects was examined. Some of the minimal designs are expandable to designs with characteristics similar to those of the recommended designs. The program is designed to facilitate the selection of the designs and the interpretation of the results and to prevent or detect problems such as drifting of responses. (C) 1998 Elsevier Science B.V. All rights reserved.

Abstract: A strategy to perform ruggedness tests for mainly procedure related factors is described. The different steps in the set-up of the experiments and in the interpretation of the results are given. The described strategy is based on a number of case studies and allows a statistical interpretation of the significance of the effects. It was implemented in a software tool. This original strategy was completed with a number of minimal screening designs which reduce the number of experiments to perform, but in consequence only allow a limited or no statistical interpretation of the effects. Some of the minimal designs are expandable to designs with characteristics similar to those of the original strategy. (C) 1998 Elsevier Science B.V. All rights reserved.

Abstract: The first step in a ruggedness test is the selection of factors to be examined and their levels. In this paper, both topics are discussed, thereby completing a strategy described earlier. It is demonstrated, by means of some examples, that depending on the formulation (definition) of a factor, information that is physically more or less meaningful is extracted from the experimental design results. Among others, the inclusion of the compounds of a buffer and of the components of a mixture in a screening design were examined. A general guideline to select the levels of the factors in a ruggedness test was proposed. Some special cases? i.e. asymmetric intervals around the nominal level, were also discussed. (C) 1998 Elsevier Science B.V. All rights reserved.

Abstract: A strategy to perform ruggedness tests for mainly procedure related factors is described. The different steps in the set-up of the experiments and in the interpretation of the results are given. The described strategy is based on a number of case studies and allows a statistical interpretation of the significance of the effects. It was implemented in a software tool. This original strategy was completed with a number of minimal screening designs which reduce the number of experiments to perform, but in consequence only allow a limited or no statistical interpretation of the effects. Some of the minimal designs are expandable to designs with characteristics similar to those of the original strategy.

Abstract: The first step in a ruggedness test is the selection of factors to be examined and their levels. In this paper, both topics are discussed, thereby completing a strategy described earlier. It is demonstrated, by means of some examples, that depending on the formulation (definition) of a factor, information that is physically more or less meaningful is extracted from the experimental design results. Among others, the inclusion of the compounds of a buffer and of the components of a mixture in a screening design were examined. A general guideline to select the levels of the factors in a ruggedness test was proposed. Some special cases, i.e. asymmetric intervals around the nominal level, were also discussed.

Abstract: A computer program is described for the experimental set-up and interpretation of ruggedness tests. The implemented strategy was based on a number of case studies and contains both recommended designs and minimal designs. The minimal designs reduce the number of experiments, but they cannot be statistically interpreted based on the interaction or dummy factor effects. The use of randomization tests as an alternative statistical interpretation method for the significance of the effects was examined. Some of the minimal designs are expandable to designs with characteristics similar to those of the recommended designs. The program is designed to facilitate the selection of the designs and the interpretation of the results and to prevent or detect problems such as drifting of responses.