Handbook of data ming - 数据分析与数据科学 - 经管之家(原人大经济论坛)

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yecm2000 发表于 2007-7-21 00:45:00 |只看作者 |坛友微信交流群|倒序 |AI写论文

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关键词：Data Ming handbook Book Data Hand handbook Data Ming

好书

这的确是一本学习数据挖掘的好书。 
多谢了！。

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板凳

yecm2000 发表于 2007-7-24 04:55:00 |只看作者 |坛友微信交流群

The contents of this book are:
I: METHODOLOGIES OF DATA MINING 1 Decision Trees 3 Johannes Gehrke Introduction 3 Problem Definition 4 Classification Tree Construction 7 Split Selection 7 Data Access 8 Tree Pruning 15 Missing Values 17 A Short Introduction to Regression Trees 20 Problem Definition 20 Split Selection 20 Data Access 21 Applications and Available Software 22 Cataloging Sky Objects 22 Decision Trees in Today’s Data Mining Tools 22 Summary 22 References 23 2 Association Rules 25 Geoffrey I. Webb Introduction 26 Market Basket Analysis 26 Association Rule Discovery 27 The Apriori Algorithm 28 The Power of the Frequent Item Set Strategy 29 Measures of Interestingness 31
Lift 31 Leverage 32 Item Set Discovery 32 Techniques for Frequent Item Set Discovery 33 Closed Item Set Strategies 33 Long Item Sets 35 Sampling 35 Techniques for Discovering Association Rules without Item Set Discovery 35 Associations with Numeric Values 36 Applications of Association Rule Discovery 36 Summary 37 References 38 3 Artificial Neural Network Models for Data Mining 41 Jennie Si, Benjamin J. Nelson, and George C. Runger Introduction to Multilayer Feedforward Networks 42 Gradient Based Training Methods for MFN 43 The Partial Derivatives 44 Nonlinear Least Squares Methods 45 Batch versus Incremental Learning 47 Comparison of MFN and Other Classification Methods 47 Decision Tree Methods 47 Discriminant Analysis Methods 48 Multiple Partition Decision Tree 49 A Growing MFN 50 Case Study 1—Classifying Surface Texture 52 Experimental Conditions 52 Quantitative Comparison Results of Classification Methods 53 Closing Discussions on Case 1 55 Introduction to SOM 55 The SOM Algorithm 56 SOM Building Blocks 57 Implementation of the SOM Algorithm 58 Case Study 2—Decoding Monkey’s Movement Directions from Its Cortical Activities 59 Trajectory Computation from Motor Cortical Discharge Rates 60 Using Data from Spiral Tasks to Train the SOM 62 Using Data from Spiral and Center→Out Tasks to Train the SOM 62 Average Testing Result Using the Leave-K-Out Method 63 Closing Discussions on Case 2 64 Final Conclusions and Discussions 65 References 65 4 Statistical Analysis of Normal and Abnormal Data 67 Connie M. Borror Introduction 67 Univariate Control Charts 68 Variables Control Charts 68 Attributes Control Charts 81
Cumulative Sum Control Charts 89 Exponentially Weighted Moving Average Control Charts 93 Choice of Control Charting Techniques 95 Average Run Length 96 Multivariate Control Charts 98 Data Description 98 Hotelling T2 Control Chart 98 Multivariate EWMA Control Charts 101 Summary 102 References 102 5 Bayesian Data Analysis 103 David Madigan and Greg Ridgeway Introduction 104 Fundamentals of Bayesian Inference 104 A Simple Example 104 A More Complicated Example 106 Hierarchical Models and Exchangeability 109 Prior Distributions in Practice 111 Bayesian Model Selection and Model Averaging 113 Model Selection 113 Model Averaging 114 Model Assessment 114 Bayesian Computation 115 Importance Sampling 115 Markov Chain Monte Carlo (MCMC) 116 An Example 117 Application to Massive Data 118 Importance Sampling for Analysis of Massive Data Sets 118 Variational Methods 120 Bayesian Modeling 121 BUGS and Models of Realistic Complexity via MCMC 121 Bayesian Predictive Modeling 125 Bayesian Descriptive Modeling 127 Available Software 128 Discussion and Future Directions 128 Summary 128 Acknowledgments 129 References 129 6 Hidden Markov Processes and Sequential Pattern Mining 133 Steven L. Scott Introduction to Hidden Markov Models 134 Parameter Estimation in the Presence of Missing Data 136 The EM Algorithm 136 MCMC Data Augmentation 138 Missing Data Summary 140 Local Computation 140 The Likelihood Recursion 140
The Forward-Backward Recursions 141 The Viterbi Algorithm 142 Understanding the Recursions 143 A Numerical Example Illustrating the Recursions 143 Illustrative Examples and Applications 144 Fetal Lamb Movements 144 The Business Cycle 150 HMM Stationary and Predictive Distributions 153 Stationary Distribution of dt 153 Predictive Distributions 154 Posterior Covariance of h 154 Available Software 154 Summary 154 References 155 7 Strategies and Methods for Prediction 159 Greg Ridgeway Introduction to the Prediction Problem 160 Guiding Examples 160 Prediction Model Components 161 Loss Functions—What We are Trying to Accomplish 162 Common Regression Loss Functions 162 Common Classification Loss Functions 163 Cox Loss Function for Survival Data 166 Linear Models 167 Linear Regression 168 Classification 169 Generalized Linear Model 172 Nonlinear Models 174 Nearest Neighbor and Kernel Methods 174 Tree Models 177 Smoothing, Basis Expansions, and Additive Models 179 Neural Networks 182 Support Vector Machines 183 Boosting 185 Availability of Software 188 Summary 189 References 190 8 Principal Components and Factor Analysis 193 Daniel W. Apley Introduction 194 Examples of Variation Patterns in Correlated Multivariate Data 194 Overview of Methods for Identifying Variation Patterns 197 Representation and Illustration of Variation Patterns in Multivariate Data 197 Principal Components Analysis 198 Definition of Principal Components 199 Using Principal Components as Estimates of the Variation Patterns 199
Factor Rotation 202 Capabilities and Limitations of PCA 202 Methods for Factor Rotation 203 Blind Source Separation 205 The Classic Blind Source Separation Problem 205 Blind Separation Principles 206 Fourth-Order Blind Separation Methods 208 Additional Manufacturing Applications 211 Available Software 211 Summary 212 References 212 9 Psychometric Methods of Latent Variable Modeling 215 Edward Ip, Igor Cadez, and Padhraic Smyth Introduction 216 Basic Latent Variable Models 217 The Basic Latent Class Model 217 The Basic Finite Mixture Model 221 The Basic Latent Trait Model 224 The Basic Factor Analytic Model 226 Common Structure 229 Extension for Data Mining 229 Extending the Basic Latent Class Model 229 Extending the Basic Mixture Model 232 Extending the Latent Trait Model 233 Extending the Factor Analytic Model 234 An Illustrative Example 236 Hierarchical Structure in Transaction Data 236 Individualized Mixture Models 237 Data Sets 238 Experimental Results 238 References and Tools 241 References 241 Tools 243 Summary 244 References 244 10 Scalable Clustering 247 Joydeep Ghosh Introduction 248 Clustering Techniques: A Brief Survey 249 Partitional Methods 250 Hierarchical Methods 255 Discriminative versus Generative Models 256 Assessment of Results 256 Visualization of Results 258 Clustering Challenges in Data Mining 259 Transactional Data Analysis 259
Next Generation Clickstream Clustering 260 Clustering Coupled Sequences 261 Large Scale Remote Sensing 261 Scalable Clustering for Data Mining 262 Scalability to Large Number of Records or Patterns, N 262 Scalability to Large Number of Attributes or Dimensions, d 264 Balanced Clustering 266 Sequence Clustering Techniques 266 Case Study: Similarity Based Clustering of Market Baskets and Web Logs 267 Case Study: Impact of Similarity Measures on Web Document Clustering 270 Similarity Measures: A Sampler 270 Clustering Algorithms and Text Data Sets 272 Comparative Results 273 Clustering Software 274 Summary 274 Acknowledgments 274 References 275 11 Time Series Similarity and Indexing 279 Gautam Das and Dimitrios Gunopulos Introduction 279 Time Series Similarity Measures 281 Euclidean Distances and L p Norms 281 Normalization Transformations 282 General Transformations 282 Dynamic Time Warping 283 Longest Common Subsequence Similarity 284 Piecewise Linear Representations 287 Probabilistic Methods 288 Other Similarity Measures 288 Indexing Techniques for Time Series 289 Indexing Time Series When the Distance Function Is a Metric 290 A Survey of Dimensionality Reduction Techniques 292 Similar Time-Series Retrieval When the Distance Function Is Not a Metric 299 Subsequence Retrieval 301 Summary 302 References 302 12 Nonlinear Time Series Analysis 305 Ying-Cheng Lai, Zonghua Liu, Nong Ye, and Tolga Yalcinkaya Introduction 305 Embedding Method for Chaotic Time Series Analysis 307 Reconstruction of Phase Space 307 Computation of Dimension 309 Detection of Unstable Periodic Orbits 311 Computing Lyapunov Exponents from Time Series 317 Time-Frequency Analysis of Time Series 323 Analytic Signals and Hilbert Transform 324 Method of EMD 331
Summary 338 Acknowledgment 338 References 338 13 Distributed Data Mining 341 Byung-Hoon Park and Hillol Kargupta Introduction 342 Related Research 343 Data Distribution and Preprocessing 344 Homogeneous/Heterogeneous Data Scenarios 345 Data Preprocessing 345 Distributed Data Mining Algorithms 346 Distributed Classifier Learning 346 Collective Data Mining 349 Distributed Association Rule Mining 350 Distributed Clustering 351 Privacy Preserving Distributed Data Mining 352 Other DDM Algorithms 353 Distributed Data Mining Systems 353 Architectural Issues 354 Communication Models in DDM Systems 356 Components Maintenance 356 Future Directions 357 References 358 II: MANAGEMENT OF DATA MINING 14 Data Collection, Preparation, Quality, and Visualization 365 Dorian Pyle Introduction 366 How Data Relates to Data Mining 366 The “10 Commandments” of Data Mining 368 What You Need to Know about Algorithms Before Preparing Data 369 Why Data Needs to be Prepared Before Mining It 370 Data Collection 370 Choosing the Right Data 370 Assembling the Data Set 371 Assaying the Data Set 372 Assessing the Effect of Missing Values 373 Data Preparation 374 Why Data Needs Preparing: The Business Case 374 Missing Values 375 Representing Time: Absolute, Relative, and Cyclic 376 Outliers and Distribution Normalization 377 Ranges and Normalization 378 Numbers and Categories 379 Data Quality 380 What Is Quality? 382 Enforcing Quality: Advantages and Disadvantages 384 Data Quality and Model Quality 384
Data Visualization 384 Seeing Is Believing 385 Absolute Versus Relative Visualization 388 Visualizing Multiple Interactions 391 Summary 391 15 Data Storage and Management 393 Tong (Teresa) Wu and Xiangyang (Sean) Li Introduction 393 Text Files and Spreadsheets 395 Text Files for Data 395 Spreadsheet Files 395 Database Systems 397 Historical Databases 397 Relational Database 398 Object-Oriented Database 399 Advanced Topics in Data Storage and Management 402 OLAP 402 Data Warehouse 403 Distributed Databases 404 Available Software 406 Summary 406 Acknowledgments 407 References 407 16 Feature Extraction, Selection, and Construction 409 Huan Liu, Lei Yu, and Hiroshi Motoda Introduction 410 Feature Extraction 411 Concepts 411 Algorithms 412 An Example 413 Summary 413 Feature Selection 414 Concepts 414 Algorithm 415 An Example 416 Summary 417 Feature Construction 417 Concepts 417 Algorithms and Examples 418 Summary 419 Some Applications 420 Summary 421 References 422 17 Performance Analysis and Evaluation 425 Sholom M. Weiss and Tong Zhang Overview of Evaluation 426 Training versus Testing 426
Measuring Error 427 Error Measurement 427 Error from Regression 428 Error from Classification 429 Error from Conditional Density Estimation 429 Accuracy 430 False Positives and Negatives 430 Precision, Recall, and the F Measure 430 Sensitivity and Specificity 431 Confusion Tables 431 ROC Curves 432 Lift Curves 432 Clustering Performance: Unlabeled Data 432 Estimating Error 433 Independent Test Cases 433 Significance Testing 433 Resampling and Cross-Validation 435 Bootstrap 436 Time Series 437 Estimating Cost and Risk 437 Other Attributes of Performance 438 Training Time 438 Application Time 438 Interpretability 438 Expert Evaluation 439 Field Testing 439 Cost of Obtaining Labeled Data 439 References 439 18 Security and Privacy 441 Chris Clifton Introduction: Why There Are Security and Privacy Issues with Data Mining 441 Detailed Problem Analysis, Solutions, and Ongoing Research 442 Privacy of Individual Data 442 Fear of What Others May Find in Otherwise Releasable Data 448 Summary 451 References 451 19 Emerging Standards and Interfaces 453 Robert Grossman, Mark Hornick, and Gregor Meyer Introduction 453 XML Standards 454 XML for Data Mining Models 454 XML for Data Mining Metadata 456 APIs 456 SQL APIs 456 Java APIs 457 OLE DB APIs 457
Web Standards 457 Semantic Web 457 Data Web 458 Other Web Services 458 Process Standards 458 Relationships 458 Summary 459 References 459 III: APPLICATIONS OF DATA MINING 20 Mining Human Performance Data 463 David A. Nembhard Introduction and Overview 463 Mining for Organizational Learning 464 Methods 464 Individual Learning 467 Data on Individual Learning 468 Methods 468 Individual Forgetting 474 Distributions and Patterns of Individual Performance 474 Other Areas 476 Privacy Issues for Human Performance Data 477 References 477 21 Mining Text Data 481 Ronen Feldman Introduction 482 Architecture of Text Mining Systems 483 Statistical Tagging 485 Text Categorization 485 Term Extraction 489 Semantic Tagging 489 DIAL 491 Development of IE Rules 493 Auditing Environment 499 Structural Tagging 500 Given 500 Find 500 Taxonomy Construction 501 Implementation Issues of Text Mining 505 Soft Matching 505 Temporal Resolution 506 Anaphora Resolution 506 To Parse or Not to Parse? 507 Database Connectivity 507 Visualizations and Analytics for Text Mining 508 Definitions and Notations 508 Category Connection Maps 509
Relationship Maps 510 Trend Graphs 516 Summary 516 References 517 22 Mining Geospatial Data 519 Shashi Shekhar and Ranga Raju Vatsavai Introduction 520 Spatial Outlier Detection Techniques 521 Illustrative Examples and Application Domains 521 Tests for Detecting Spatial Outliers 522 Solution Procedures 525 Spatial Colocation Rules 525 Illustrative Application Domains 526 Colocation Rule Approaches 527 Solution Procedures 530 Location Prediction 530 An Illustrative Application Domain 530 Problem Formulation 532 Modeling Spatial Dependencies Using the SAR and MRF Models 533 Logistic SAR 534 MRF Based Bayesian Classifiers 535 Clustering 537 Categories of Clustering Algorithms 539 K-Medoid: An Algorithm for Clustering 540 Clustering, Mixture Analysis, and the EM Algorithm 541 Summary 544 Acknowledgments 545 References 545 23 Mining Science and Engineering Data 549 Chandrika Kamath Introduction 550 Motivation for Mining Scientific Data 551 Data Mining Examples in Science and Engineering 552 Data Mining in Astronomy 552 Data Mining in Earth Sciences 555 Data Mining in Medical Imaging 557 Data Mining in Nondestructive Testing 557 Data Mining in Security and Surveillance 558 Data Mining in Simulation Data 558 Other Applications of Scientific Data Mining 561 Common Challenges in Mining Scientific Data 561 Potential Solutions to Some Common Problems 562 Data Registration 564 De-Noising Data 565 Object Identification 566 Dimensionality Reduction 567 Generating a Good Training Set 568 Software for Scientific Data Mining 568
Summary 569 References 569 24 Mining Data in Bioinformatics 573 Mohammed J. Zaki Introduction 574 Background 574 Basic Molecular Biology 574 Mining Methods in Protein Structure Prediction 575 Mining Protein Contact Maps 577 Classifying Contacts Versus Noncontacts 578 Mining Methodology 578 How Much Information Is There in Amino Acids Alone? 581 Using Local Structures for Contact Prediction 582 Characterizing Physical, Protein-Like Contact Maps 587 Generating a Database of Protein-Like Structures 588 Mining Dense Patterns in Contact Maps 589 Pruning and Integration 590 Experimental Results 591 Future Directions for Contact Map Mining 593 Heuristic Rules for “Physicality” 593 Rules for Pathways in Contact Map Space 594 Summary 595 References 596 25 Mining Customer Relationship Management (CRM) Data 597 Robert Cooley Introduction 597 Data Sources 599 Data Types 599 E-Commerce Data 601 Data Preparation 604 Data Aggregation 605 Feature Preparation 607 Pattern Discovery 608 Pattern Analysis and Deployment 610 Robustness 610 Interestingness 611 Deployment 611 Sample Business Problems 612 Strategic Questions 612 Operational Questions 613 Summary 615 References 616 26 Mining Computer and Network Security Data 617 Nong Ye Introduction 618 Intrusive Activities and System Activity Data 618
Phases of Intrusions 619 Data of System Activities 620 Extraction and Representation of Activity Features for Intrusion Detection 623 Features of System Activities 624 Feature Representation 625 Existing Intrusion Detection Techniques 628 Application of Statistical Anomaly Detection Techniques to Intrusion Detection 629 Hotelling’s T 2 Test and Chi-Square Distance Test 629 Data Source and Representation 631 Application of Hotelling’s T 2 Test and Chi-Square Distance Test 633 Testing Performance 633 Summary 634 References 635 27 Mining Image Data 637 Chabane Djeraba and Gregory Fernandez Introduction 637 Related Works 639 Method 641 How to Discover the Number of Clusters: k 641 K-Automatic Discovery Algorithm 644 Clustering Algorithm 646 Experimental Results 646 Data Sets 647 Data Item Representation 648 Evaluation Method 649 Results and Analysis 650 Summary 654 References 655 28 Mining Manufacturing Quality Data 657 Murat C. Testik and George C. Runger Introduction 657 Multivariate Control Charts 658 Hotelling T 2 Control Charts 658 MEWMA Charts 660 Nonparametric Properties of the MEWMA Control Charts 663 Summary 667 References 668 Author Index 669 Subject Index 681

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