Silhouettes: a graphical aid to the interpretation and validation of cluster analysis

Machine Learning is the study of methods for programming computers to learn. Computers are applied to a wide range of tasks, and for most of these it is relatively easy for programmers to design and implement the necessary software. However, there are many tasks for which this is difficult or impossible. These can be divided into four general categories. First, there are problems for which there exist no human experts. For example, in modern automated manufacturing facilities, there is a need to predict machine failures before they occur by analyzing sensor readings. Because the machines are new, there are no human experts who can be interviewed by a programmer to provide the knowledge necessary to build a computer system. A machine learning system can study recorded data and subsequent machine failures and learn prediction rules. Second, there are problems where human experts exist, but where they are unable to explain their expertise. This is the case in many perceptual tasks, such as speech recognition, hand-writing recognition, and natural language understanding. Virtually all humans exhibit expert-level abilities on these tasks, but none of them can describe the detailed steps that they follow as they perform them. Fortunately, humans can provide machines with examples of the inputs and correct outputs for these tasks, so machine learning algorithms can learn to map the inputs to the outputs. Third, there are problems where phenomena are changing rapidly. In finance, for example, people would like to predict the future behavior of the stock market, of consumer purchases, or of exchange rates. These behaviors change frequently, so that even if a programmer could construct a good predictive computer program, it would need to be rewritten frequently. A learning program can relieve the programmer of this burden by constantly modifying and tuning a set of learned prediction rules. Fourth, there are applications that need to be customized for each computer user separately. Consider, for example, a program to filter unwanted electronic mail messages. Different users will need different filters. It is unreasonable to expect each user to program his or her own rules, and it is infeasible to provide every user with a software engineer to keep the rules up-to-date. A machine learning system can learn which mail messages the user rejects and maintain the filtering rules automatically. Machine learning addresses many of the same research questions as the fields of statistics, data mining, and psychology, but with differences of emphasis. Statistics focuses on understanding the phenomena that have generated the data, often with the goal of testing different hypotheses about those phenomena. Data mining seeks to find patterns in the data that are understandable by people. Psychological studies of human learning aspire to understand the mechanisms underlying the various learning behaviors exhibited by people (concept learning, skill acquisition, strategy change, etc.).

Machine learning

Chapter 1 The Biosphere Chapter 2 The Anthroposphere Introduction Air Pollution Water Pollution Soil Plants Chapter 3 Soils and Soil Processes Introduction Weathering Processes Pedogenic Processes Chapter 4 Soil Constituents Introduction Trace Elements Minerals Organic Matter Organisms in Soils Chapter 5 Trace Elements in Plants Introduction Absorption Translocation Availability Essentiality and Deficiency Toxicity and Tolerance Speciation Interaction Chapter 6 Elements of Group 1 (Previously Group Ia) Introduction Lithium Rubidium Cesium Chapter 7 Elements of Group 2 (Previously Group IIa) Beryllium Strontium Barium Radium Chapter 8 Elements of Group 3 (Previously Group IIIb) Scandium Yttrium Lanthanides Actinides Chapter 9 Elements of Group 4 (Previously Group IVb) Titanium Zirconium Hafnium Chapter 10 Elements of Group 5 (Previously Group Vb) Vanadium Niobium Tantalum Chapter 11 Elements of Group 6 (Previously Group VIb) Chromium Molybdenum Tungsten Chapter 12 Elements of Group 7 (Previously Group VIIb) Manganese Technetium Rhenium Chapter 13 Elements of Group 8 (Previously Part of Group VIII) Iron Ruthenium Osmium Chapter 14 Elements of Group 9 (Previously Part of Group VIII) Cobalt Rhodium Iridium Chapter 15 Elements of Group 10 (Previously Part of Group VIII) Nickel Palladium Platinum Chapter 16 Elements of Group 11 (Previously Group Ib) Copper Silver Gold Chapter 17 Trace Elements of Group 12 (Previously of Group IIb) Zinc Cadmium Mercury Chapter 18 Elements of Group 13 (Previously Group IIIa) Boron Aluminum Gallium Indium Thallium Chapter 19 Elements of Group I4 (Previously Group IVa) Silicon Germanium Tin Lead Chapter 20 Elements of Group 15 (Previously Group Va) Arsenic Antimony Bismuth Chapter 21 Elements of Group 16 (Previously Group VIa) Selenium Tellurium Polonium Chapter 22 Elements of Group 17 (Previously Group VIIa) Fluorine Chlorine Bromine Iodine

Trace elements in soils and plants

This survey provides an overview of higher-order tensor decompositions, their applications, and available software. A tensor is a multidimensional or $N$-way array. Decompositions of higher-order tensors (i.e., $N$-way arrays with $N \geq 3$) have applications in psycho-metrics, chemometrics, signal processing, numerical linear algebra, computer vision, numerical analysis, data mining, neuroscience, graph analysis, and elsewhere. Two particular tensor decompositions can be considered to be higher-order extensions of the matrix singular value decomposition: CANDECOMP/PARAFAC (CP) decomposes a tensor as a sum of rank-one tensors, and the Tucker decomposition is a higher-order form of principal component analysis. There are many other tensor decompositions, including INDSCAL, PARAFAC2, CANDELINC, DEDICOM, and PARATUCK2 as well as nonnegative variants of all of the above. The N-way Toolbox, Tensor Toolbox, and Multilinear Engine are examples of software packages for working with tensors.

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Tensor Decompositions and Applications

1. Introduction and Overview. 2. Detecting Influential Observations and Outliers. 3. Detecting and Assessing Collinearity. 4. Applications and Remedies. 5. Research Issues and Directions for Extensions. Bibliography. Author Index. Subject Index.

Regression Diagnostics: Identifying Influential Data and Sources of Collinearity

Handbook of Chemometrics and Qualimetrics: Part A

1. Chemometrics and the Analytical Process. 2. Precision and Accuracy. 3. Evaluation of Precision and Accuracy. Comparison of Two Procedures. 4. Evaluation of Sources of Variation in Data. Analysis of Variance. 5. Calibration. 6. Reliability and Drift. 7. Sensitivity and Limit of Detection. 8. Selectivity and Specificity. 9. Information. 10. Costs. 11. The Time Constant. 12. Signals and Data. 13. Regression Methods. 14. Correlation Methods. 15. Signal Processing. 16. Response Surfaces and Models. 17. Exploration of Response Surfaces. 18. Optimization of Analytical Chemical Methods. 19. Optimization of Chromatographic Methods. 20. The Multivariate Approach. 21. Principal Components and Factor Analysis. 22. Clustering Techniques. 23. Supervised Pattern Recognition. 24. Decisions in the Analytical Laboratory. 25. Operations Research. 26. Decision Making. 27. Process Control. Appendix. Subject Index.

Chemometrics: A Textbook

Introduction. Statistical Description of the Quality of Processes and Measurements. The Normal Distribution. An Introduction to Hypothesis Testing. Some Important Hypothesis Tests. Analysis of Variance. Control Charts. Straight Line Regression and Calibration. Vectors and Matrices. Multiple and Polynomial Regression. Non-linear Regression. Robust Statistics. Internal Method Validation. Method Validation by Interlaboratory Studies. Other Distributions. The 2 2 Contingency Table. Principal Components. Information Theory. Fuzzy Methods. Process Modelling and Sampling. An Introduction to Experimental Design. Two-level Factorial Designs. Fractional Factorial Designs. Multi-level Designs. Mixture Designs. Other Optimization Methods. Genetic Algorithms and Other Global Search Strategies. Index.

Handbook of Chemometrics and Qualimetrics

A new method for the elimination of uninformative variables in multivariate data sets is proposed. To achieve this, artificial (noise) variables are added and a closed form of the PLS or PCR model is obtained for the data set containing the experimental and the artificial variables. The experimental variables that do not have more importance than the artificial variables, as judged from a criterion based on the b coefficients, are eliminated. The performance of the method is evaluated on simulated data. Practical aspects are discussed on experimentally obtained near-IR data sets. It is concluded that the elimination of uninformative variables can improve predictive ability.

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Elimination of Uninformative Variables for Multivariate Calibration

A practical, user-oriented introduction to clustering methods, including both hierarchical and non-hierarchical methods. Shows how clustering can be used to interpret large quantities of analytical data and discusses the relation of clustering to other pattern recognition techniques. Uses a two-level approach to provide both a qualitative understanding of the philosophy, advantages, and disadvantages of clustering and a quantitative understanding for readers who want a strong mathematical background. Includes a worked example and a list of computer packages.

Desire Massart

Papers

Handbook of Chemometrics and Qualimetrics: Part A

Chemometrics: A Textbook

Handbook of Chemometrics and Qualimetrics

Elimination of Uninformative Variables for Multivariate Calibration

The Interpretation of Analytical Chemical Data by the Use of Cluster Analysis