A Unique Book on Data Mining of Enterprise Data: Algorithms and Applications. Edited by Dr's T.Warren Liao and Evangelos Triantaphyllou from LSU.

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 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 Naļve 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 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 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 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 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 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 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 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 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 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 CHAPTER 12 Data Mining Techniques for Improving Workflow Model, by D. Gunopulos and S. Subramaniam.....545 Click here for the abstract of this Chapter in PDF format 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 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 CHAPTER 14 Support Vector Machines and Applications, by T. B. Trafalis and O. O. Oladunni..........643 Click here for the abstract of this Chapter in PDF format 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 CHAPTER 15 A Survey of Manifold-Based Learning Methods, by X. Huo, X. Ni, and A. K. Smith.............691 Click here for the abstract of this Chapter in PDF format 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 CHAPTER 16 Predictive Regression Modeling for Small Enterprise Data Sets with Bootstrap, Clustering, and Bagging, by C. J. Feng and K. Erla..................................747 Click here for the abstract of this Chapter in PDF format 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 Subject Index...................................................775 List of Contributors............................................779 About the Editors...............................................785

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