The increasing volume of data in modern business and science calls for more complex and sophisticated tools. Although advances in data mining technology have made extensive data collection much easier, it's still always evolving and there is a constant need for new techniques and tools that can help us transform this data into useful information and knowledge. Since the previous edition's publication, great advances have been made in the field of data mining. Not only does the third of edition of Data Mining: Concepts and Techniques continue the tradition of equipping you with an understanding and application of the theory and practice of discovering patterns hidden in large data sets, it also focuses on new, important topics in the field: data warehouses and data cube technology, mining stream, mining social networks, and mining spatial, multimedia and other complex data. Each chapter is a stand-alone guide to a critical topic, presenting proven algorithms and sound implementations ready to be used directly or with strategic modification against live data. This is the resource you need if you want to apply today's most powerful data mining techniques to meet real business challenges. * Presents dozens of algorithms and implementation examples, all in pseudo-code and suitable for use in real-world, large-scale data mining projects. * Addresses advanced topics such as mining object-relational databases, spatial databases, multimedia databases, time-series databases, text databases, the World Wide Web, and applications in several fields. *Provides a comprehensive, practical look at the concepts and techniques you need to get the most out of real business data

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Data Mining: Concepts and Techniques

Reigning measures of psychological well-being have little theoretical grounding, despite an extensive literature on the contours of positive functioning. Aspects of well-being derived from this literature (i.e., self-acceptance, positive relations with others, autonomy, environmental mastery, purpose in life, and personal growth) were operationalized. Three hundred and twenty-one men and women, divided among young, middle-aged, and older adults, rated themselves on these measures along with six instruments prominent in earlier studies (i.e., affect balance, life satisfaction, self-esteem, morale, locus of control, depression). Results revealed that positive relations with others, autonomy, purpose in life, and personal growth were not strongly tied to prior assessment indexes, thereby supporting the claim that key aspects of positive functioning have not been represented in the empirical arena. Furthermore, age profiles revealed a more differentiated pattern of well-being than is evident in prior research.

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Happiness is everything, or is it? Explorations on the meaning of psychological well-being.

An essential textbook for any student or researcher in biology needing to design experiments, sample programs or analyse the resulting data The text begins with a revision of estimation and hypothesis testing methods, covering both classical and Bayesian philosophies, before advancing to the analysis of linear and generalized linear models Topics covered include linear and logistic regression, simple and complex ANOVA models (for factorial, nested, block, split-plot and repeated measures and covariance designs), and log-linear models Multivariate techniques, including classification and ordination, are then introduced Special emphasis is placed on checking assumptions, exploratory data analysis and presentation of results The main analyses are illustrated with many examples from published papers and there is an extensive reference list to both the statistical and biological literature The book is supported by a website that provides all data sets, questions for each chapter and links to software

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Experimental Design and Data Analysis for Biologists

https://www.wsl.ch/staff/niklaus.zimmermann/papers/ecomod135_147.pdf

Predictive habitat distribution models in ecology

Predicting the distribution of endangered species from habitat data is frequently perceived to be a useful technique. Models that predict the presence or absence of a species are normally judged by the number of prediction errors. These may be of two types: false positives and false negatives. Many of the prediction errors can be traced to ecological processes such as unsaturated habitat and species interactions. Consequently, if prediction errors are not placed in an ecological context the results of the model may be misleading. The simplest, and most widely used, measure of prediction accuracy is the number of correctly classified cases. There are other measures of prediction success that may be more appropriate. Strategies for assessing the causes and costs of these errors are discussed. A range of techniques for measuring error in presence/absence models, including some that are seldom used by ecologists (e.g. ROC plots and cost matrices), are described. A new approach to estimating prediction error, which is based on the spatial characteristics of the errors, is proposed. Thirteen recommendations are made to enable the objective selection of an error assessment technique for ecological presence/absence models.

https://www.cambridge.org/core/services/aop-cambridge-core/content/view/S0376892997000088

A review of methods for the assessment of prediction errors in conservation presence/absence models

Partial table of contents: Discriminant Analysis in Research. PREDICTION. Basic Ideas of Classification. Multivariate Normal Rules. Classification Results. Hit Rate Estimation. Nonnormal Rules. Reporting Results of a PDA. Applications of PDA. DESCRIPTION. Group Separation. Assessing Effects. Describing Effects. Selecting and Ordering Variables. Reporting Results of a DDA. Applications of DDA. ISSUES AND PROBLEMS. Special Problems. Appendices. Answers to Exercises. References. Index.

Applied discriminant analysis

The argument for preceding multiple analysis of variance (ANOVAS) with a multivariate analysis of variance (MANOVA) to control for Type I error is challenged. Several situations are discussed in which multiple ANOVAS might be conducted without the necessity of a preliminary MANOVA. Three reasons for considering a multivariate analysis are discussed: to identify outcome variable system constructs, to select variable subsets, and to determine variable relative worth. The analyses discussed in this article are those appropriate in research situations in which analysis of variance techniques are useful. These analyses are used to study the effects of treatment variables on outcome/response variables (in ex post facto as well as experimental studies). We speak of an univariate analysis of variance (ANOVA) when a single outcome variable is involved; when multiple outcome variables are involved, it is a multivariate analysis of variance (MANOVA). (Covariance analyses may also be included.) With multiple outcome variables, the typical analysis approach used in the group-comparison context, at least in the behavioral sciences, is to either (a) conduct multiple ANOVAs or (b) conduct a MANOVA followed by multiple ANOVAS. That these are two popular choices may be concluded from a survey of some prominent behavioral science journals. The 1986 issues

Multivariate analysis versus multiple univariate analyses.

Articles published in several prominent educational journals were examined to investigate the use of data analysis tools by researchers in four research paradigms: between-subjects univariate designs, between-subjects multivariate designs, repeated measures designs, and covariance designs. In addition to examining specific details pertaining to the research design (e.g., sample size, group size equality/inequality) and methods employed for data analysis, the authors also catalogued whether (a) validity assumptions were examined, (b) effect size indices were reported, (c) sample sizes were selected on the basis of power considerations, and (d) appropriate textbooks and/or articles were cited to communicate the nature of the analyses that were performed. The present analyses imply that researchers rarely verify that validity assumptions are satisfied and that, accordingly, they typically use analyses that are nonrobust to assumption violations. In addition, researchers rarely report effect size statistics, ...

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Statistical Practices of Educational Researchers: An Analysis of their ANOVA, MANOVA, and ANCOVA Analyses:

Publisher Summary This chapter provides an overview of some of the conceptual details related to multivariate analysis of variance (MANOVA) and multivariate analysis of covariance (MANCOVA) using a design involving one or two grouping variables and a collection of response variables that may include some concomitant variables. The chapter begins with a discussion of the purposes of multivariate analyses and describe research situations found in the behavioral science literature that call for the use of a MANOVA or a MANCOVA. However, MANCOVA is more involved than MANOVA from three standpoints: substantive theory, study design, and data analysis. Next, the chapter describes some MANOVA design aspects with a focus on the initial choice of a response variable system and on sampling. It also discusses at length a number of suggested guidelines for data analysis strategies and for reporting and interpreting MANOVA results, and illustrates these guidelines using a research example. The chapter concludes with some recommended practices regarding the typical use of MANOVA and MANCOVA by applied researchers.

Multivariate analysis of variance and covariance.

List of Figures List of Tables Preface to Second Edition Acknowledgments Preface to First Edition Notation I INTRODUCTION 1 Discriminant Analysis in Research 11 A Little History 12 Overview 13 Descriptive Discriminant Analysis 14 Predictive Discriminant Analysis 15 Design in Discriminant Analysis 2 Preliminaries 21 Introduction 22 Research Context 23 Data, Analysis Units, Variables, and Constructs 24 Summarizing Data 25 Matrix Operations 26 Distance 27 Linear Composite 28 Probability 29 Statistical Testing 210 Judgment in Data Analysis 211 Summary II ONE-FACTOR MANOVA/DDA 3 Group Separation 31 Introduction 32 Two-Group Analyses 33 Test for Covariance Matrix Equality 34 Yao Test 35 Multiple-Group Analyses-Single Factor 36 Computer Application 37 Summary 4 Assessing MANOVA Effects 41 Introduction 42 Strength of Association 43 Computer Application I 44 Group Contrasts 45 Computer Application II 46 Covariance Matrix Heterogeneity 47 Sample Size 48 Summary 5 Describing MANOVA Effects 51 Introduction 52 Omnibus Effects 53 Computer Application I 54 Standardized LDFWeights 55 LDF Space Dimension 56 Computer Application II 57 Computer Application III 58 Contrast Effects 59 Computer Application IV 510 Summary 6 Deleting and Ordering Variables 61 Introduction 62 Variable Deletion 63 Variable Ordering 64 Contrast Analyses 65 Computer Application II 66 Comments 7 Reporting DDA Results 71 Introduction 72 Example of Reporting DDA Results 73 Computer Package Information 74 Reporting Terms 75 MANOVA/DDA Applications 76 Concerns 77 Overview III FACTORIAL MANOVA, MANCOVA, AND REPEATED MEASURES 8 Factorial MANOVA 81 Introduction 82 Research Context 83 Univariate Analysis 84 Multivariate Analysis 85 Computer Application I 86 Computer Application II 87 Nonorthogonal Design 88 Outcome Variable Ordering and Deletion 89 Summary 9 Analysis of Covariance 91 Introduction 92 Research Context 93 Univariate ANCOVA 94 Multivariate ANCOVA (MANCOVA) 95 Computer Application I 96 Comparing Adjusted Means-Omnibus Test 97 Computer Application II 98 Contrast Analysis 99 Computer Application III 910 Summary 10 Repeated-Measures Analysis 101 Introduction 102 Research Context 103 Univariate Analyses 104 Multivariate Analysis 105 Computer Application I 106 Univariate and Multivariate Analyses 107 Testing for Sphericity 108 Computer Application II 109 Contrast Analysis 1010 Computer Application III 1011 Summary 11 Mixed-Model Analysis 111 Introduction 112 Research Context 113 Univariate Analysis 114 Multivariate Analysis 115 Computer Application I 116 Contrast Analysis 117 Computer Application II 118 Summary IV GROUP MEMBERSHIP PREDICTION 12 Classification Basics 121 Introduction 122 Notion of Distance 123 Distance and Classification 124 Classification Rules in General 125 Comments 13 Multivariate Normal Rules 131 Introduction 132 Normal Density Functions 133 Classification Rules Based on Normality 134 Classification Functions 135 Summary of Classification Statistics 136 Choice of Rule Form 137 Comments 14 Classification Results 141 Introduction 142 Research Context 143 Computer Application 144 Individual Unit Results 145 Group Results 146 Comments 15 Hit Rate Estimation 151 Introduction 152 True Hit Rates 153 Hit Rate Estimators 154 Computer Application 155 Choice of Hit Rate Estimator 156 Outliers and In-Doubt Units 157 Sample Size 158 Comments 16 Effectiveness of Classification Rules 161 Introduction 162 Proportional Chance Criterion 163 Maximum-Chance Criterion 164 Improvement over Chance 165 Comparison of Rules 166 Computer Application I 167 Effect of Unequal Priors 168 PDA Validity/Reliability 169 Applying a Classification Rule to New Units 1610 Comments 17 Deleting and Ordering Predictors 171 Introduction 172 Predictor Deletion 173 Computer Application 174 Predictor Ordering 175 Reanalysis 176 Comments 177 Side Note 18 Two-Group Classification 181 Introduction 182 Two-Group Rule 183 Regression Analogy 184 MRA-PDA Relationship 185 Necessary Sample Size 186 Univariate Classification 19 Nonnormal Rules 191 Introduction 192 Continuous Variables 193 Categorical Variables 194 Predictor Mixtures 195 Comments 20 Reporting PDA Results 201 Introduction 202 Example of Reporting PDA Results 203 Some Additional Specific PDA Information 204 Computer Package Information 205 Reporting Terms 206 Sources of PDA Applications 207 Concerns 208 Overview Further Reading Exercises 21 PDA-Related Analyses 211 Introduction 212 Nonlinear Methods 213 Other Methods V ISSUES AND PROBLEMS 22 Issues in PDA and DDA 221 Introduction 222 Five Choices in PDA 223 Stepwise Analyses 224 StandardizedWeights Versus Structure r's 225 Data-Based Structure 23 Problems in PDA and DDA 231 Introduction 232 Missing Data 233 Outliers and Influential Observations 234 Initial Group Misclassification 235 Misclassification Costs 236 Statistical Versus Clinical Prediction 237 Other Problems Appendix A: Data Set Descriptions Appendix B: Some DA-Related Originators Appendix C: List of Computer Syntax Appendix D: Contents ofWileyWebsite References Answers to Exercises Index

Carl J. Huberty

Papers

Applied discriminant analysis

Multivariate analysis versus multiple univariate analyses.

Statistical Practices of Educational Researchers: An Analysis of their ANOVA, MANOVA, and ANCOVA Analyses:

Multivariate analysis of variance and covariance.

Applied MANOVA and discriminant analysis