A Unique Book on the Data Mining of Enterprise Data:  Algorithms and Applications
Recent Advances in Data Mining of Enterprise Data: Algorithms and Applications


by T. Warren Liao and Evangelos Triantaphyllou (Editors)


An edited book published in 2007 by World Scientific Publishing Company, Singapore, in its Computers and Operations Research series, Vol. 6.

ISBN-10: 981277985X
ISBN-13: 978-9812779854






Summary of the Scope of this Book:
The main goal of the new field of data mining is the analysis of large and complex datasets. Some very important datasets may be derived from business and industrial activities. This kind of data is known as “enterprise data.” The common characteristic of such datasets is that the analyst wishes to analyze them for the purpose of designing a more cost-effective strategy for optimizing some type of performance measure, such as reducing production time, improving quality, eliminating wastes, or maximizing profit. Data in this category may describe different scheduling scenarios in a manufacturing environment, quality control of some process, fault diagnosis in the operation of a machine or process, risk analysis when issuing credit to applicants, management of supply chains in a manufacturing system, or data for business related decision-making.

Key Features:
  • The chapters have been written by top researchers / practitioners in the corresponding fields.
  • The problems related to the mining of enterprise data are studied comprehensively and in depth.
  • Each chapter is self-contained and thorough on the topic it studies. They are also complementary of each other.
  • The developments are presented in a complete and intuitive manner.
  • At the end of each chapter there is a discussion of challenges for future research.




  • Table of Contents (as part of this Webpage)



  • PDF files:
  • Foreword (as a downloadable PDF file)


  • Preface (as a downloadable PDF file)


  • Acknowledgments (as a downloadable PDF file)


  • Table of Contents (as a downloadable PDF file)


  • Subject Index (as a downloadable PDF file)


  • List of Contributors (as a downloadable PDF file)


  • About the Editors (as a downloadable PDF file)




  • Purchasing Information from Amazon.com:


    TABLE OF CONTENTS
    Foreword..........................................................xxi    
    Preface...........................................................xxiii 
    Acknowledgments...................................................xxxi  
    
    
    CHAPTER 1
    Enterprise Data Mining – A Review and Research 
    Directions, by T. W. Liao.......................1
    Click here for the abstract of this Chapter in PDF format
    1.    Introduction................................................2
    2.    The Basics of Data Mining and Knowledge Discovery...........6
           2.1.   Data mining and the knowledge discovery process.....6
           2.2    Data mining algorithms/methodologies................9
           2.3    Data mining system architectures...................12
           2.4    Data mining software programs......................14
    3.    Types and Characteristics of Enterprise Data...............17
    4.    Overview of the Enterprise Data Mining Activities..........23
           4.1    Customer related...................................23
           4.2    Sales related......................................30
           4.3    Product related....................................37
           4.4    Production planning and control related............43
           4.5    Logistics related..................................51
           4.6    Process related....................................55
                    4.6.1   For the semi-conductor industry..........55
                    4.6.2   For the electronics industry.............63
                    4.6.3   For the process industry.................72
                    4.6.4   For other industries.....................79
           4.7    Others.............................................83
           4.8    Summary............................................87
                   4.8.1   Data type, size, and sources..............87
                   4.8.2   Data preprocessing........................88
    5.    Discussion.................................................90
    6.    Research Programs and Directions...........................91
           6.1   On e-commerce and web mining........................91
           6.2   On customer-related mining..........................92
           6.3   On sales-related mining.............................93
           6.4   On product-related mining...........................94
           6.5   On process-related mining...........................94
           6.6   On the use of text mining in enterprise systems.....95
    References.......................................................96
    Author’s Biographical Statement.................................109
    
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    CHAPTER 2
    Application and Comparison of Classification 
    Techniques in Controlling Credit Risk, 
    by  L. Yu, G. Chen, A. Koronios, S. Zhu, 
    and X. Guo....................................111
    Click here for the abstract of this Chapter in PDF format
    1.    Credit Risk and Credit Rating.............................112
    2.    Data and Variables........................................115
    3.    Classification Techniques.................................115
           3.1   Logistic regression................................116
           3.2   Discriminant analysis..............................117
           3.3   K-nearest neighbors................................119
           3.4   Naďve Bayes........................................120
           3.5   The TAN technique..................................121
           3.6   Decision trees.....................................122
           3.7   Associative classification.........................124
           3.8   Artificial neural networks.........................126
           3.9   Support vector machines............................129
    4.    An Empirical Study........................................131
           4.1   Experimental settings..............................131
           4.2   The ROC curve and the Delong-Pearson method........133
           4.3   Experimental results...............................135
    5.    Conclusions and Future Work...............................139
    References......................................................140
    Authors' Biographical Statements................................144
    
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    CHAPTER 3
    Predictive Classification with Imbalanced 
    Enterprise Data, by S. Daskalaki, I. Kopanas, 
    and N. M. Avouris.............................147
    Click here for the abstract of this Chapter in PDF format
    1.    Introduction..............................................148
    2.    Enterprise Data and Predictive Classification.............151
    3.    The Process of Knowledge Discovery from Enterprise Data...154
           3.1   Definition of the problem and application domain...155
           3.2   Creating a target database.........................156
           3.3   Data cleaning and preprocessing....................157
           3.4   Data reduction and projection......................159
           3.5   Defining the data mining function and 
    				performance measures............160
           3.6   Selection of data mining algorithms................163
           3.7   Experimentation with data mining algorithms........164
           3.8   Combining classifiers and interpretation of 
    					     the results........167
           3.9   Using the discovered knowledge.....................171
    4.    Development of a Cost-Based Evaluation Framework..........171
    5.    Operationalization of the Discovered Knowledge: Design 
    	of an Intelligent Insolvencies Management System........178
    6.    Summary and Conclusions...................................181
    References......................................................183
    Authors' Biographical Statements................................187
    
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    CHAPTER 4
    Using Soft Computing Methods for Time Series 
    Forecasting, by P.-C. Chang and Y.-W. Wang....189
    Click here for the abstract of this Chapter in PDF format
    1.    Introduction..............................................190
          1.1   Background and motives..............................190
          1.2   Objectives..........................................191
    2.   Literature Review..........................................191
          2.1   Traditional time series forecasting research........191
          2.2   Neural network based forecasting methods............192
          2.3   Hybridizing a genetic algorithm (GA) with a 
    			neural network for forecasting..........193
                  2.3.1   Using a GA to design the NN architecture..193
                  2.3.2   Using a GA to generate the NN 
    			connection weights......................194
          2.4   Review of sales forecasting search..................194
    3.   Problem Definition.........................................200
          3.1   Scope of the research data..........................200
          3.2   Characteristics of the variables considered.........200
                  3.2.1   Macroeconomic domain......................200
                  3.2.2   Downstream demand domain..................201
                  3.2.3   Industrial production domain..............202
                  3.2.4   Time series domain........................202
          3.3   Performance index...................................202
    4.   Methodology................................................203
          4.1   Data preprocessing..................................203
                  4.1.1   Gray relation analysis....................203
                  4.1.2   Winter’s exponential smoothing............207
          4.2   Evolving neural networks (ENN)......................209
                  4.2.1   ENN modeling..............................209
                  4.2.2   ENN parameters design.....................214
          4.3   Weighted evolving fuzzy neural networks (WEFuNN)....218
                  4.3.1   Building of the WEFuNN....................218
                             4.3.1.1   The feed-forward learning 
    						phase...........220
                             4.3.1.2   The forecasting phase........226
                  4.3.2   WEFuNN parameters design..................227
    5.   Experimental Results.......................................229
          5.1   Winter’s exponential smoothing......................230
          5.2   The Back propagation neural network model...........230
          5.3   Multiple regression analysis model..................231
          5.4   Evolving fuzzy neural network model.................232
          5.5   Evolving neural network.............................233
          5.6   Comparisons.........................................235
    6.   Conclusions................................................236
    References......................................................237
    Appendix........................................................243
    Authors’ Biographical Statements................................246
    
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    CHAPTER 5
    Data Mining Applications of Process Platform 
    Formation for High Variety Production, 
    by J. Jiao and L. Zhang.......................247
    Click here for the abstract of this Chapter in PDF format
    1.    Background................................................248
    2.    Methodology...............................................249
    3.    Routing Similarity Measure................................251
           3.1   Node content similarity measure....................251
                   3.1.1   Material similarity measure..............252
                              3.1.1.1   Procedure for calculating 
    					similarities between 
    					primitive components....253
                              3.1.1.2   Procedure for calculating 
    					similarities between 
    					compound components.....257
                   3.1.2   Product similarity measure...............258
                   3.1.3   Resource similarity measure..............258
                   3.1.4   Operation similarity and node content 
    					similarity measures.....259
                   3.1.5   Normalized node content similarity 
    					matrix..................260
          3.2    Tree structure similarity measure..................261
          3.3    Routing similarity measure.........................265
    4.   Routing Clustering.........................................265
    5.   Routing Unification........................................267
          5.1    Basic routing elements.............................267
          5.2    Master and selective routing elements..............267
          5.3    Basic tree structures..............................268
          5.4    Tree growing.......................................269
    6.   A Case Study...............................................275
          6.1   The routing similarity measure......................275
          6.2   The routing clustering..............................281
          6.3   The routing unification.............................282
    7.   Summary....................................................283
    References......................................................284
    Authors’ Biographical Statements................................286
    
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    CHAPTER 6
    A Data Mining Approach to Production Control  
    in Dynamic Manufacturing Systems, 
    by C. Kwak and Y. Yih.........................287
    Click here for the abstract of this Chapter in PDF format
    1.   Introduction...............................................288
    2.   Previous Approaches to Scheduling of Wafer Fabrication.....291
    3.   Simulation Model and Solution Methodology..................294
          3.1    Simulation model...................................292
          3.2    Development of a scheduler.........................298
                   3.2.1    Decision variables and decision rules...298
                   3.2.2    Evaluation criteria: system 
    				performance and status..........300
                   3.2.3    Data collection: a simulation approach..300
                   3.2.4    Data classification: a competitive 
    				neural network approach.........301
                   3.2.5    Selection of decision rules for 
    				decision variables..............306
    4.   An Experimental Study......................................306
          4.1    Experimental design................................306
          4.2    Results and analyses...............................309
    5.   Related Studies............................................313
    6.   Conclusions................................................317
    References......................................................319
    Authors’ Biographical Statements................................321
    
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    CHAPTER 7
    Predicting Wine Quality from Agricultural 
    Data with Single-Objective and 
    Multi-Objective Data Mining Algorithms, 
    by M. Last, S. Elnekave, A. Naor, and 
    V. Schoenfeld.................................323
    Click here for the abstract of this Chapter in PDF format
    1.   Introduction...............................................324
    2.   Problem Description........................................325
    3.   Information Networks and the Information Graph.............329
          3.1   An extended classification task.....................329
          3.2   Single-objective information networks...............330
          3.3   Multi-objective information networks................336
          3.4   Information graphs..................................338
    4.   A Case Study: the Cabernet Sauvignon problem...............342
          4.1   Data selection......................................342
          4.2   Data preprocessing..................................344
                  4.2.1    Ripening data............................344
                  4.2.2    Meteorological measurements..............347
          4.3   Design of data mining runs..........................349
          4.4   Sing-objective models...............................350
          4.5   Multi-objective models..............................353
          4.6   Comparative evaluation..............................355
          4.7   The discovered knowledge and its potential use......357
    5.   Related Work...............................................358
          5.1    Mining of agricultural data........................358
          5.2    Multi-objective classification models and 
    						algorithms......359
    6.   Conclusions................................................361
    References......................................................362
    Authors’ Biographical Statements................................364
    
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    CHAPTER 8
    Enhancing Competitive Advantages and 
    Operational Excellence for High-Tech 
    Industry through Data Mining and Digital 
    Management, by C.-F. Chien and S.-C. Hsu......367
    Click here for the abstract of this Chapter in PDF format
    1.   Introduction...............................................368
    2.   Knowledge Discovery in Databases and Data Mining...........370
          2.1    Problem types for data mining in the high-tech 
    						industry........373
          2.2    Data mining methodologies..........................374
                   2.2.1    Decision trees..........................374
                               2.2.1.1    Decision tree 
    						construction....375
                               2.2.1.2    CART......................379
                               2.2.1.3    C4.5......................380
                               2.2.1.4    CHAID.....................382
                   2.2.2    Artificial neural networks..............383
                               2.2.2.1    Associate learning 
    						networks........386
                               2.2.2.2    Supervised learning 
    						networks........388
                               2.2.2.3    Unsupervised learning 
    						networks........390
    3.   Application of Data Mining in Semiconductor Manufacturing..393
          3.1    Problem definition.................................393
          3.2    Types of data mining applications..................395
                   3.2.1    Extracting characteristics from 
    						WAT data........396
                   3.2.2    Process failure diagnosis of CP and 
    					engineering data........397
                   3.2.3    Process failure diagnosis of WAT and 
    					engineering data........398
                   3.2.4    Extracting characteristics from 
    			semiconductor manufacturing data........399
          3.3    A Hybrid decision tree approach for CP low 
    					yield diagnosis.........400
          3.4    Key stage screening................................402
          3.5    Construction of the Decision tree..................404
     4.   Conclusions...............................................406
    References......................................................407
    Authors' Biographical Statements................................411
    
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    CHAPTER 9
    Multivariate Control Charts from 
    a Data Mining Perspective, by G. C. Porzio 
    and G. Ragozini...............................413
    Click here for the abstract of this Chapter in PDF format
    1.   Introduction...............................................414
    2.   Control Charts and Statistical Process Control Phases......415
    3.   Multivariate Statistical Process Control...................419
          3.1    The sequential quality control setting.............419
          3.2    The Hotelling T2 Control Chart.....................421
    4.   Is the T2  Statistic Really Able to Tackle Data 
    					Mining Issues?..........424
          4.1    Many data, many outliers...........................424
          4.2    Questioning the assumptions on shape and 
    						distribution....430
    5.   Designing Nonparametric Charts When Large HDS 
    		Are Available: the Data Depth Approach..........434
          5.1    Data depth and control charts......................436
          5.2    Towards a parametric setting for data depth 
    					control charts..........438
          5.3    A Shewhart chart for changes in location and 
    					increases in scale......442
          5.4    An illustrative example............................443
          5.5    Average run length functions for data depth 
    					control charts..........446
          5.6    A simulation study of chart performance............448
          5.7    Choosing an empirical depth function...............453
    6.   Final Remarks..............................................454
    References......................................................456
    Authors’ Biographical Statements................................462
    
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    CHAPTER 10
    Data Mining of Multi-Dimensional Functional 
    Data for Manufacturing Fault Diagnosis, 
    by M. K. Jeong, S. G. Kong, and 
    O. A. Omitaomu................................463
    Click here for the abstract of this Chapter in PDF format
    1.   Introduction...............................................464
    2.   Data Mining of Functional Data.............................465
          2.1    Dimensionality reduction techniques for 
    					functional data.........465
          2.2    Multi-scale fault diagnosis........................468
                   2.2.1   A case study: data mining of 
    					functional data.........469
          2.3    Motor shaft misalignment prediction based on 
    					functional data.........472
                   2.3.1   Techniques for predicting with high 
    					number of predictors....474
                   2.3.2   A case study: motor shaft misalignment 
    						prediction......477
    3.   Data Mining in Hyperspectral Imaging.......................481
          3.1    A hyperspectral fluorescence imaging system........483
          3.2    Hyperspectral image dimensionality reduction.......485
          3.3    Spectral band selection............................490
          3.4    A case study: data mining in hyperspectral 
    						imaging.........494
    4.   Conclusions................................................496
    References......................................................497
    Authors’ Biographical Statements................................503
    
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    CHAPTER 11
    Maintenance Planning Using Enterprise Data 
    Mining,by L. P. Khoo, Z. W. Zhong, 
    and H. Y. Lim.................................505
    Click here for the abstract of this Chapter in PDF format
    1.   Introduction...............................................506
    2.   Rough Sets, Genetic Algorithms, and Tabu Search............508
          2.1    Rough sets.........................................508
                   2.1.1   Overview.................................508
                   2.1.2   Rough sets and fuzzy sets................509
                   2.1.3   Applications.............................510
                   2.1.4   The strengths of the theory of 
    						rough sets......511
                   2.1.5   Enterprise information and the 
    					information system......512
          2.2    Genetic algorithms.................................516
          2.3    Tabu search........................................520
    3.   The Proposed Hybrid Approach...............................521
         3.1    Background..........................................521
         3.2     The rough set engine...............................521
         3.3     The tabu-enhanced GA engine........................523
         3.4     Rule organizer.....................................528
    4.   A Case Study...............................................528
         4.1     Background.........................................528
                   4.1.1    Mounting bracket failures...............531
                   4.1.2    The alignment problem...................532
                   4.1.3    Sea/land inner/outer guide roller 
    						failures........532
         4.2     Analysis using the proposed hybrid approach........532
         4.3     Discussion.........................................537
                   4.3.1    Validity of the extracted rules.........537
                   4.3.2    A comparative analysis of the results...538
    5.   Conclusions................................................540
    References......................................................541
    Authors’ Biographical Statements................................544
    
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    CHAPTER 12  
    Data Mining Techniques for Improving Workflow
    Model, by D. Gunopulos and S. Subramaniam.....545 
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    1.   Introduction...............................................546
    2.   Workflow Models............................................549
    3.   Discovery of Models from Workflow Logs.....................552
    4.   Managing Flexible Workflow Systems.........................555
    5.   Workflow Optimization Through Mining of Workflow Logs......557
          5.1     Repositioning decision points.....................557
          5.2     Prediction of execution paths.....................560
    6.   Capturing the Evolution of Workflow Models.................565
    7.   Applications in Software Engineering.......................566
          7.1     Discovering reasons for bugs in software 
    						processes.......567
          7.2     Predicting the control flow of a software 
    		process for efficient resource management.......568
    8.   Conclusions................................................569
    References......................................................569
    Authors’ Biographical Statements................................576
    
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    CHAPTER 13
    Mining Images of Cell-Based Assays, 
    by P. Perner..................................577
    Click here for the abstract of this Chapter in PDF format
    1.   Introduction...............................................578
    2.   The Application Used for the Demonstration of the 
    					System Capability.......580
    3.   Challenges and Requirements for the Systems................582
    4.   The Cell-Interpret’s Architecture..........................582
    5.   Case-Based Image Segmentation..............................584
          5.1     The case-based reasoning unit.....................585
          5.2     Management of case bases..........................587
    6.   Feature Extraction.........................................588
          6.1    Our flexible texture descriptor....................589
    7.   The Decision-Tree Induction Unit...........................591
          7.1    The basic principle................................591
          7.2    Terminology of the decision tree...................592
          7.3    Subtasks and decision criteria for decision-tree 
    						induction.......594
          7.4    Attribute selection criteria.......................597
                   7.4.1    Information gain criteria and the 
    						gain ratio......598
                   7.4.2    The Gini function.......................600
          7.5    Discretization of attribute values.................601
                   7.5.1    Binary discretization...................603
                               7.5.1.1    Binary discretization 
    					based on entropy........603
                               7.5.1.2    Discretization based on 
    					inter- and intra-class 
    						variance........604
                   7.5.2    Multi-interval discretization...........605
                               7.5.2.1    The basic algorithm.......606
                               7.5.2.2    Determination of the 
    					number of intervals.....606
                               7.5.2.3    Cluster-utility criteria..607
                               7.5.2.4    MLD-based criteria........607
                               7.5.2.5    LVQ-based discretization..608
                               7.5.2.6    Histogram-based 	
    						discretization..609
                               7.5.2.7    Chi-merge discretization..610
                   7.5.3    The influence of discretization 
    			methods on the resulting decision tree..612
                   7.5.4    Discretization of categorical or 
    					symbolic attributes.....614
                               7.5.4.1    Manual abstraction of 
    					attribute values........614
                               7.5.4.2    Automatic aggregation.....615
          7.6    Pruning............................................615
                   7.6.1    Overview of pruning methods.............617
                   7.6.2    Cost-complexity pruning.................617
          7.7    Some general remarks...............................618
    8.   The Case-Based Reasoning Unit..............................621
    9.   Concept Clustering as Knowledge Discovery..................623
    10. The Overall Image Mining Procedure..........................627
          10.1  A case study........................................629
          10.2  Brainstorming and image catalogue...................629
          10.3  The interviewing process............................630
          10.4  Collection of image descriptions into 
    				the database....................630
          10.5  The image-mining experiment.........................631
          10.6  Review..............................................634
          10.7  Lessons learned.....................................635
    11.  Conclusions and Future Work................................636
    References......................................................637
    Author's Biographical Statement.................................641
    
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    CHAPTER 14 
    Support Vector Machines and Applications, 
    by T. B. Trafalis and O. O. Oladunni..........643
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    1.    Introduction..............................................644
    2.    Fundamentals of Support Vector Machines...................646
           2.1    Linear separability...............................646
           2.2    Linear inseparability.............................649
           2.3    Nonlinear separability............................652
           2.4    Numerical testing.................................654
                    2.4.1    The AND problem........................654
                    2.4.2    The XOR problem........................656
    3.    Least Squares Support Vector Machines.....................657
    4.    Multi-Classification Support Vector Machines..............662
           4.1    The one-against-all (OAA) method..................662
           4.2    The one-against-one method........................664
           4.3    Pairwise multi-classification support vector 
    						machines........665
           4.4    Further techniques based on central 
    			representation of the version space.....672
    5.    Some Applications.........................................674
           5.1    Enterprise modeling (novelty detection)...........674
           5.2    Non-enterprise modeling application 
    					(multiphase flow).......679
    6.   Conclusions................................................681
    References......................................................682
    Authors’ Biographical Statements................................689
    
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    CHAPTER 15 
    A Survey of Manifold-Based Learning Methods,
    by X. Huo, X. Ni, and A. K. Smith.............691
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    1.    Introduction..............................................692
    2.    Survey of Existing Methods................................694
           2.1     Group 1: Principal component analysis............695
           2.2     Group 2: Semi-classical methods – 
    				multidimensional scaling........697
                     2.2.1     Solving MDS as an eigenvalue 
    						problem.........698
           2.3     Group 3: Manifold searching methods..............699
                     2.3.1     Generative topographic mapping.......699
                     2.3.2     Locally linear embedding.............701
                     2.3.3     ISOMAP...............................703
           2.4     Group 4: Methods from spectral theory............704
                     2.4.1    Laplacian eigenmaps...................704
                     2.4.2    Hessian eigenmaps.....................706
            2.5    Group 5: Methods based on global alignment.......707
    3.    Unification via the Null-Space Method.....................708
            3.1    LLE as a null-space based method.................709
            3.2    LTSA as a null-space based method................711
            3.3    Comparison between LTSA and LLE..................713
    4.    Principles Guiding the Methodological Developments........713
            4.1    Sufficient dimension reduction...................713
            4.2    Desired statistical properties...................714
                     4.2.1     Consistency..........................714
                     4.2.2     Rate of convergence..................715
                     4.2.3     Exhaustiveness.......................715
                     4.2.4     Robustness...........................716
            4.3    Initial results..................................716
                     4.3.1     Formulation and related open 
    						questions.......716
                     4.3.2     Consistency of LTSA..................718
    5.    Examples and Potential Applications.......................722
           5.1     Successes of manifold based methods on 
    					synthetic data..........722
                     5.1.1     Examples of LTSA recovering 
    				implicit parameterization.......722
                     5.1.2     Examples of LLP in denoising.........724
           5.2    Curve clustering..................................725
           5.3    Image detection...................................728
                    5.3.1     Formulation...........................731
                    5.3.2     Distance to manifold..................732
                    5.3.3     SRA: the significance run algorithm...733
                    5.3.4     Parameter estimation..................734
                                 5.3.4.1     Number of nearest 
    						neighbors.......734
                                 5.3.4.2     Local dimension........734
                    5.3.5     Simulations...........................736
                    5.3.6     Discussion............................738
           5.4    Application on the localization of 
    					sensor networks.........738
    6.    Conclusions...............................................740
    References......................................................741
    Authors’ Biographical Statements................................745
    
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    CHAPTER 16
    Predictive Regression Modeling for Small 
    Enterprise Data Sets with Bootstrap, 
    Clustering, and Bagging, by C. J. Feng  
    and  K. Erla..................................747
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    1.    Introduction..............................................748
    2.    Literature Review		750
           2.1     Tree-based classifiers and the 
    					bootstrap 0.632 rule....750
           2.2     Bagging..........................................751
    3.    Methodology...............................................753
           3.1     The data modeling procedure......................753
           3.2     Bootstrap sampling...............................753
           3.3     Selecting the best subset regression model.......756
           3.4     Evaluation of prediction errors..................758
                     3.4.1     Prediction error evaluation..........758
                     3.4.2     The 0.632 prediction error...........759
           3.5     Cluster analysis.................................760
           3.6     Bagging..........................................760
    4.    A Computational Study.....................................761
           4.1     The experimental data............................761
           4.2     Computational results............................761
    5.   Conclusions................................................770
    References......................................................771
    Authors’ Biographical Statements................................774
    
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    Subject Index...................................................775
    
    List of Contributors............................................779
    
    About the Editors...............................................785
    
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