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Recent Advances in Data Mining of Enterprise Data: Algorithms and Applications 6689tp.indd 11/26/07 4:11:02 PM Series on Computers and Operations Research Series Editor: P M Pardalos (University of Florida) Published Vol Optimization and Optimal Control eds P M Pardalos, I Tseveendorj and R Enkhbat Vol Supply Chain and Finance eds P M Pardalos, A Migdalas and G Baourakis Vol Marketing Trends for Organic Food in the 21st Century ed G Baourakis Vol Theory and Algorithms for Cooperative Systems eds D Grundel, R Murphey and P M Pardalos Vol Application of Quantitative Techniques for the Prediction of Bank Acquisition Targets by F Pasiouras, S K Tanna and C Zopounidis Vol Recent Advances in Data Mining of Enterprise Data: Algorithms and Applications eds T Warren Liao and Evangelos Triantaphyllou Vol Computer Aided Methods in Optimal Design and Operations eds I D L Bogle and J Zilinskas Steven - Recent Adv in Data.pmd 12/4/2007, 1:51 PM Series on Computers and Operations Research Vol Recent Advances in Data Mining of Enterprise Data: Algorithms and Applications T Warren Liao Evangelos Triantaphyllou Louisiana State University, USA World Scientific NEW JERSEY 6689tp.indd • LONDON • SINGAPORE • BEIJING • SHANGHAI • HONG KONG • TA I P E I • CHENNAI 11/26/07 4:11:03 PM Published by World Scientific Publishing Co Pte Ltd Toh Tuck Link, Singapore 596224 USA office: 27 Warren Street, Suite 401-402, Hackensack, NJ 07601 UK office: 57 Shelton Street, Covent Garden, London WC2H 9HE British Library Cataloguing-in-Publication Data A catalogue record for this book is available from the British Library RECENT ADVANCES IN DATA MINING OF ENTERPRISE DATA: Algorithms and Applications Series on Computers and Operations Research — Vol Copyright © 2007 by World Scientific Publishing Co Pte Ltd All rights reserved This book, or parts thereof, may not be reproduced in any form or by any means, electronic or mechanical, including photocopying, recording or any information storage and retrieval system now known or to be invented, without written permission from the Publisher For photocopying of material in this volume, please pay a copying fee through the Copyright Clearance Center, Inc., 222 Rosewood Drive, Danvers, MA 01923, USA In this case permission to photocopy is not required from the publisher ISBN-13 978-981-277-985-4 ISBN-10 981-277-985-X Printed in Singapore Steven - Recent Adv in Data.pmd 12/4/2007, 1:51 PM I wish to dedicate this book to my wife, Chi-fen, for her commitment to be my partner and her devotion to assist me developing my career and becoming a better person She is extremely patient and tolerant with me and takes excellent care of our two kids, Allen and Karen, while I am too busy to spend time with them, especially during my first sabbatical year and during the time of editing this book I would also like to dedicate this book to my mother, Mo-dan Lien, and my late father, Shu-min, for their understanding, support, and encouragement to pursue my dream Lastly, my dedication goes to Alli, my daughter’s beloved cat, for her playfulness and the joy she brings to the family ─ T Warren Liao I gratefully dedicate this book to Juri; my life’s inspiration, to my mother Helen and late father John (Ioannis), my brother Andreas, my late grandfather Evangelos, and also to my immensely beloved Ragus and Ollopa (“Ikasinilab, Shiakun”) Ollopa was helping with this project all the way until the very last days of his wonderful life, which ended exactly when this project ended He will always live in our memories This book is also dedicated to his beloved family from Takarazuka This book would have never been prepared without Juri’s, Ragus’ and Ollapa’s continuous encouragement, patience, and unique inspiration ─ Evangelos (Vangelis) Triantaphyllou Contents Foreword Preface Acknowledgements xxi xxiii xxxi Chapter Enterprise Data Mining: A Review and Research Directions, by T W Liao Introduction The Basics of Data Mining and Knowledge Discovery 2.1 Data mining and the knowledge discovery process 2.2 Data mining algorithms/methodologies 2.3 Data mining system architectures 2.4 Data mining software programs Types and Characteristics of Enterprise Data Overview of the Enterprise Data Mining Activities 4.1 Customer related 4.2 Sales related 4.3 Product related 4.4 Production planning and control related 4.5 Logistics related 4.6 Process related 4.6.1 For the semi-conductor industry 4.6.2 For the electronics industry 4.6.3 For the process industry 4.6.4 For other industries 4.7 Others 4.8 Summary 4.8.1 Data type, size, and sources 4.8.2 Data preprocessing Discussion vii 6 12 14 17 23 23 30 37 43 51 55 55 63 72 79 83 87 87 88 90 viii Recent Advances in Data Mining of Enterprise Data Research Programs and Directions 6.1 On e-commerce and web mining 6.2 On customer-related mining 6.3 On sales-related mining 6.4 On product-related mining 6.5 On process-related mining 6.6 On the use of text mining in enterprise systems References Author’s Biographical Statement 91 91 92 93 94 94 95 96 109 Chapter Application and Comparison of Classification Techniques in Controlling Credit Risk, by L Yu, G Chen, A Koronios, S Zhu, and X Guo Credit Risk and Credit Rating Data and Variables Classification Techniques 3.1 Logistic regression 3.2 Discriminant analysis 3.3 K-nearest neighbors 3.4 Naïve Bayes 3.5 The TAN technique 3.6 Decision trees 3.7 Associative classification 3.8 Artificial neural networks 3.9 Support vector machines An Empirical Study 4.1 Experimental settings 4.2 The ROC curve and the Delong-Pearson method 4.3 Experimental results Conclusions and Future Work References Authors’ Biographical Statements 111 112 115 115 116 117 119 120 121 122 124 126 129 131 131 133 135 139 140 144 Chapter Predictive Classification with Imbalanced Enterprise Data, by S Daskalaki, I Kopanas, and N M Avouris Introduction Enterprise Data and Predictive Classification The Process of Knowledge Discovery from Enterprise Data 3.1 Definition of the problem and application domain 3.2 Creating a target database 3.3 Data cleaning and preprocessing 147 148 151 154 155 156 157 Contents 3.4 Data reduction and projection 3.5 Defining the data mining function and performance measures 3.6 Selection of data mining algorithms 3.7 Experimentation with data mining algorithms 3.8 Combining classifiers and interpretation of the results 3.9 Using the discovered knowledge Development of a Cost-Based Evaluation Framework Operationalization of the Discovered Knowledge: Design of an Intelligent Insolvencies Management System Summary and Conclusions References Authors’ Biographical Statements Chapter Using Soft Computing Methods for Time Series Forecasting, by P.-C Chang and Y.-W Wang Introduction 1.1 Background and motives 1.2 Objectives Literature Review 2.1 Traditional time series forecasting research 2.2 Neural network based forecasting methods 2.3 Hybridizing a genetic algorithm (GA) with a neural network for forecasting 2.3.1 Using a GA to design the NN architecture 2.3.2 Using a GA to generate the NN connection weights 2.4 Review of sales forecasting research Problem Definition 3.1 Scope of the research data 3.2 Characteristics of the variables considered 3.2.1 Macroeconomic domain 3.2.2 Downstream demand domain 3.2.3 Industrial production domain 3.2.4 Time series domain 3.3 The performance index Methodology 4.1 Data preprocessing 4.1.1 Gray relation analysis 4.1.2 Winter’s exponential smoothing 4.2 Evolving neural networks (ENN) 4.2.1 ENN modeling 4.2.2 ENN parameters design ix 159 160 163 164 167 171 171 178 181 183 187 189 190 190 191 191 191 192 193 193 194 194 200 200 200 200 201 202 202 202 203 203 203 207 209 209 214 x Recent Advances in Data Mining of Enterprise Data 4.3 Weighted evolving fuzzy neural networks (WEFuNN) 4.3.1 Building of the WEFuNN 4.3.1.1 The feed-forward learning phase 4.3.1.2 The forecasting phase 4.3.2 WEFuNN parameters design Experimental Results 5.1 Winter’s exponential smoothing 5.2 The BPN model 5.3 Multiple regression analysis model 5.4 Evolving fuzzy neural network model (EFuNN) 5.5 Evolving neural network (ENN) 5.6 Comparisons Conclusions References Appendix Authors’ Biographical Statements Chapter Data Mining Applications of Process Platform Formation for High Variety Production, by J Jiao and L Zhang Background Methodology Routing Similarity Measure 3.1 Node content similarity measure 3.1.1 Material similarity measure 3.1.1.1 Procedure for calculating similarities between primitive components 3.1.1.2 Procedure for calculating similarities between compound components 3.1.2 Product similarity measure 3.1.3 Resource similarity measure 3.1.4 Operation similarity and node content similarity measures 3.1.5 Normalized node content similarity matrix 3.2 Tree structure similarity measure 3.3 ROU similarity measure ROU Clustering ROU Unification 5.1 Basic routing elements 5.2 Master and selective routing elements 5.3 Basic tree structures 5.4 Tree growing 218 218 220 226 227 229 230 230 231 232 233 235 236 237 243 246 247 248 249 251 251 252 253 257 258 258 259 260 261 265 265 267 267 267 268 269 Chapter 16 Predictive Regression Modeling for Small Enterprise Datasets 769 Table 11 A summary of key statistics for models with X1, X3, X4 (5 models) Sampling No e(pred.) PRESS R2 (pred.) Cp |Cp-p| S-40 1.74062 88.8519 97.39 4.6 0.6 S-67 1.69247 96.6109 96.89 3.4 0.6 S-68 1.70766 73.8306 95.18 3.9 0.1 S-94 1.55886 81.5902 96.77 4.8 0.8 S-96 Total Average Min Max St Dev 1.78422 8.483829 1.697 1.559 1.784 0.085 71.6009 412.4845 82.497 71.601 96.611 10.419 97.58 483.81 96.762 95.18 97.58 0.947 4.6 21.3 4.26 3.4 4.8 0.590 0.6 2.7 0.54 0.1 0.8 0.261 Table 12 A summary of key statistics for models with X1 and X2 (8 models) Sample No e(pred.) PRESS R2 (pred.) Cp |Cp-p| S-15 3.13405 70.9637 97.67 S-53 2.00489 119.374 95.9 3.8 0.8 S-65 2.19176 36.0407 98.38 3.3 0.3 S-70 1.84146 113.285 95.04 3.6 0.6 S-78 2.07613 50.4934 96.81 1.5 1.5 S-89 2.0667 101.861 93.37 4.1 1.1 S-90 1.6467 130.472 95.3 2.8 0.2 S-93 2.89413 52.6424 98.18 Total 17.8558 675.132 770.65 2.2 25.3 0.8 6.3 Average 2.2320 84.3920 96.331 3.163 0.788 Min 1.6467 36.0407 93.370 1.500 0.200 Max 3.1340 130.4720 98.380 4.100 1.500 St Dev 0.5140 36.1790 1.745 0.927 0.426 770 Recent Advances in Data Mining of Enterprise Data All these three category models have almost the same R2 and PRESS statistics Considering the prediction error, we chose the model, with X1, X2 and X3 as predictors, which has the smallest prediction error compared to the other models For example, to compare with the results of Montgomery et al (2001) that were constructed with the parameters of X1 and X2, we calculated the predicted error, PRESS, the R predicted values and the Cp statistic for those models having X1 and X2 as parameters The eight models had X1 and X2 as their predictors and the R predicted and PRESS statistics were calculated for samples with parameters X1 and X2 The results are provided in Table 13 Table 13 Comparison of the two best subset models PRESS R2 (pred) Cp |Cp-p| Our Models 18.622 1071.370 1060.290 38.800 8.600 Total 97.397 96.390 3.527 Average 1.693 0.780 45.038 94.160 3.000 Min 1.294 0.100 168.837 98.120 4.900 Max 1.837 1.000 46.836 1.5740 0.700 0.250 St Dev 0.159 Models with X1 and X2 based on Montgomery et al (2001) 17.856 675.132 770.650 25.300 6.300 Total 84.392 96.331 3.163 Average 2.232 0.790 36.041 93.370 1.500 Min 1.647 0.200 130.472 98.380 4.100 Max 3.134 1.500 36.179 1.745 0.927 0.430 St Dev 0.514 e(pred) Conclusions The 0.632 bootstrap prediction errors were larger in the cluster of models based on the selection from Montgomery et al (2001) that included predictors X1 and X2 It shows that using the 0.632 bootstrap method resulted in models with better prediction performance for small datasets than the traditional best subset regression Chapter 16 Predictive Regression Modeling for Small Enterprise Datasets 771 The cluster of models based on our selection is also better than that based on selection by Montgomery’s simple best subset approach in terms of |Cp – p|, which is preferred in selecting predictive models However, there was little difference in the R2 (prediction) and the PRESS statistics between our best models and the best models based on the selection by Montgomery et al (2001) as shown in Table 13 Since the R2 (prediction) and the PRESS statistics are computed based on the leave-one-out cross-validation (CV) method and Breiman and Spector (1992) and Zhang (1993) have concluded that they are among the worst CV methods, this comparison does not appear to be valuable Next is a list of the contributions described in this chapter: 1) The 0.632 bootstrap sampling works better than the simple subset selection when the sample size is small and no distribution can be assumed However, it involves more computational effort 2) On the average, bootstrap sampling gives better prediction error than the v fold cross- validation 3) 0.632 bootstrap sampling gives better prediction errors than bootstrap sampling Next is a list of some potential future directions in this area of research: 1) The 0.632-bootstrap sampling method was used to correct upward bias What if the samples are downwardly biased? 2) What if there is no relationship between the independent and the dependent variables? 3) How to deal with cases when training errors are zero (which is rare)? The 0.632 bootstrap method will give a prediction error of 0.632 × 0.5 + 0.368 × = 0.316 What should have been the true error? References Anonymous (2003) Statistica Data Miner User’s Manual StatSoft, Tulsa, OK, U.S.A Breiman, L (1994) Heuristics of Instability in Model Selection Technical Report, University of California at Berkeley, Berkeley, CA, U.S.A Breiman, L (1996) Bagging predictors Machine Learning, 26(2), 123-140 Breiman, L and Spector, P (1992) Submodel selection and evaluation in regression: the X-random case International Statistics Review, 60(3), 291319 772 Recent Advances in Data Mining of Enterprise Data Breiman, L., Friedman, J., Olshen, R., and Stone, C (1984) Classification and Regression Trees Wadsworth and Brooks/Cole, Pacific Grove, CA, U.S.A Burnham, K P and Anderson, D R (2002) Model Selection and Inference: A Practical Information – Theoretic Approach, 2nd Edition Springer-Verlag, New York, NY, U.S.A Clyde, M A and Lee, H K H (2001) “Bagging and the Bayesian bootstrap.” Artificial Intelligence and Statistics, T Richardson and T Jaakkola (Eds.), pp 169-174 Daniel, C and Wood, F S (1980) Fitting Equations to Data, 2nd Edition Wiley, New York, NY, U.S.A Davison, A C and Hinkley, D V (1997) Bootstrap Methods and their Applications Cambridge University Press, Cambridge, UK Draper, N R and Smith, H (1998) Applied Regression Analysis, 3rd Edition John Wiley & Sons, New York, NY, U.S.A Efron, B (1983) Estimating the error rate of a prediction rule: some improvements on cross-validation J Amer Stat Assoc 78:316–331 Efron, B (1979) Bootstrap methods: another look at the jackknife Ann Statist 7, 1-26 Efron, B and Tibshirani, R J (1993) An Introduction to the Bootstrap Chapman & Hall, London, UK Efron, B and Tibshirani R.J (1995) Cross-Validation and the Bootstrap: Estimating the Error Rate of a Prediction Rule Technical Report 176, Stanford University, Stanford, CA, U.S.A Ezekiel, M (1930) Methods of Correlation Analysis Wiley, New York, NY, U.S.A Feng, C-X and Kusiak, A (2006) Data mining applications in engineering design, manufacturing and logistics International Journal of Production Research, 44(14), 2689-2694 (July) Feng, C-X., Yu, Z-G and Kusiak, A (2006) Selection and validation of predictive regression and neural networks models based on designed experiments IIE Transactions, 38(1), 13-24 Feng, C-X., Yu, Z-G., Kingi, U., and Baig, M P (2005) Threefold vs fivefold cross validation in one-hidden-layer and two-hidden-layer predictive neural networks modeling of machining surface roughness data SME Journal of Manufacturing Systems, 24(2), 93-107 Gilmour, S G (1996) The interpretation of Mallows Cp-statistic The Statistician, 45(1): 49-56 Hald, A (1952) Statistical Theory with Engineering Applications Wiley, New York, NY, U.S.A Han, J and Kamber, M (2006) Data Mining: Concepts and Techniques, 2nd Edition Morgan Kaufmann, San Francisco, CA, U.S.A Kennard, R W (1971) A note on the Cp statistic Technometrics, 13, 899-900 Chapter 16 Predictive Regression Modeling for Small Enterprise Datasets 773 Kohavi, R (1995) A study of cross-validation and bootstrap for accuracy estimation and model selection Proc of the 14th Int Joint Conf on A.I., Vol 2, Canada Ljung, L (1999) System Identification - Theory for the User, 2nd Edition Prentice Hall, Upper Saddle River, NJ, U.S.A McQuarrie, A D R and Tsai, C-L (1998) Regression and Time Series Model Selection World Scientific, Singapore Mallows, C L (1997) Cp and prediction with many regressors: comments on Mallows (1995) Technometrics, 39(1), 115-116 Mallows, C L (1995) More comments on Cp Technometrics, 37(4), 362-372 Mallows, C L (1973) Some comments on Cp Technometrics, 15(4), 661-675 Miller, A J (2002) Subset Selection in Regression, 2nd Edition Chapman & Hall, Boca Raton, FL, U.S.A Miller, R G (1974) The jackknife – A review Biometrika, 61(1), 1-15 Montgomery, D C., Peck, E A., and Vining, G G (2001) Introduction to Linear Regression Analysis, 3rd Edition Wiley, New York, NY, U.S.A Myers, R H (1990) Classical and Modern Regression with Applications, 2nd Edition Duxbury Press, Boston, MA, U.S.A Oza, N C and Russell, S (2001) Online Bagging and Boosting Artificial Intelligence and Statistics 2001, T Richardson and T Jaakkola (Eds.), pp 105-112 Ripley, B.D (1996) Pattern Recognition and Neural Networks Cambridge University Press, Cambridge, UK Rubin, D B (1981) The Bayesian bootstrap Ann Stat 9, 130-134 Stone, M (1977) An asymptotic equivalence of choice of model by crossvalidation and Akaike’s criterion J Royal Statist Soc., B39, 44-7 Wang, G and Liao, T W (2002) Automatic identification of different types of welding defects in radiographic images, NDT&E International, 35, 519-528 Witten, I H and Frank, E (2000) Data Mining: Practical Machine Learning Tools and Techniques with Java Implementations Morgan Kaufmann Publishers, San Francisco, CA, U.S.A Zhang, P (1993) Model selection via multifold cross-validation Annals of Statistics, 21(1), 299-31 774 Recent Advances in Data Mining of Enterprise Data Authors’ Biographical Statements Dr Chang-Xue Jack Feng is a Professor of Industrial and Manufacturing Engineering at Bradley University He was a Visiting Engineering Fellow with a focus in lean assembly to the Caterpillar Production System Division of Caterpillar Inc in Peoria, Illinois between May 206 and August 2007 He has been the President of the Institute of Industrial Engineers (IIE) Central Illinois Chapter since 1999 and the President of Feng Consulting since 1995 Before joining Bradley in 1998, he was on the engineering faculty of Penn State University between 1995 and 1998 During this period, he developed, named and directed the William and Mary Hintz Manufacturing Technology Lab He received his PHD and MS degrees in industrial engineering, MS degree in manufacturing engineering, and BS degree in mechanical engineering He has applied computation tools, including statistics, optimization, computational neural networks, and fuzzy logic in integrated product and process development, lean/agile manufacturing, and quality and precision engineering Dr Feng serves on the editorial board of the International Journal of Production Research and the Open Operations Research Journal He is a senior member of ASQ, IIE, and SME and a member of ASA and INFORMS He has published more than 75 technical papers, three books and three book chapters Erla Krishna is the owner of his consulting business in training and support of the ERP software SAP He was a senior supply quality engineer in Caterpillar’s Building and Construction Products Division located in North Carolina after getting his MS IE degree from Bradley University in 2004 Subject Index case-based reasoning, 578 case-based image segmentation, 584-588 certain rules, 539 CHAID, 382-383 change point detection, 60 circuit probe data, 394 class imbalance, 92, 147, 163, 394 classification, 115-130, 151-154, 323, 325, 326, 643 classification and regression tree, 29, 41, 50, 62, 379-380, 400 classification techniques, 115-130 Clementine, 14, 15 cluster analysis, see clustering clustering, 5, 12, 13, 26, 28, 37, 40, 42, 50, 51, 57, 59, 69, 72, 73, 74, 93, 760 combination of classifiers, 167 competitive neural network, 287, 290, 298 concept clustering, 623-627 convex hull peeling, 435, 441, 449, 453 cost-based evaluation, 171-178 CP data, see circuit probe data credit rating, 111-116 credit scoring, 28, 29 CRM, see customer relationship management customer behavior, 23, 24, 25, 26, 28 customer churn, 25, 27 customer relationship management, 2, 5, 13 A adaptive resonance theory, 58 agricultural data, 323, 325, 359 ANFIS, 48 Apriori, 40 artificial neural network, See neural networks associate learning networks, 386-388 association rules, 9, 24, 25, 40, 43, 57, 93 associative classification, 114, 124, 125, 126, 139, 140 attribute-oriented induction, 9, 48 back propagation neural network, 230 B bagging, 60, 751-753 Bayesian Belief Network, 82 Bayesian model, 12, 27, 30, 59, 60, 72, 82 Boolean function, bootstrap 0.632 rule, 750-751 business process, 545 C C4.5, 12, 26, 30, 48, 49, 58, 61, 63, 82, 114, 380-382 CART, see classification and regression tree 775 776 Recent Advances in Data Mining of Enterprise Data D data selection, 342-344 data depth approach, 434-454 data mining algorithms/methodologies, 9-12, 374-390 data mining software programs, 14-17 data mining system architectures, 12-14 data normalization, 88 data preprocessing, 7, 30, 42, 57, 87, 88, 90, 203, 344-349 data reduction, 6, 11, 61, 62, 89 data transformation, 30, 58, 69, 89, 92 DBMiner, 14, 15 decision support systems, 147, 168, 171 decision trees, 5, 9, 12, 13, 15, 16, 25, 26, 27, 41, 48, 49, 51, 57, 59, 61, 63, 72, 79, 80, 81, 82, 122-124, 374-382, 591-618 defect patterns, 58, 59, 88 Delong-Pearson method, 114, 133, 135 denoising, 723-725 discriminant analysis, 27, 29, 43, 117-119 dimensionality reduction, 465-468, 485-490, 692 direct marketing, 15, 24 discrete wavelet transform, 485 discretization, 326, 350, 601-615, dispatching rule, 48, 49, 287, 290-292 due date assignment, 49 E e-commerce, 91 enterprise data, 12-23 enterprise data mining, 1, 23-90 Enterprise Miner, 14, 15, 16 evolutionary neural network, 209-218 F factor analysis, 397 fault detection, 4, 57, 61, 62, 63, 94 fault diagnosis, 4, 63, 73, 95, 463 feature extraction, 588-591, feature selection, 11, 25, 41, 69, 81, 92, 95 forecasting, 2, 36, 37, 93, 190 fraud detection, 4, 30, 92 frequent itemset, 43, 55 fuzzy clustering, 40, 73, 247, 249 fuzzy c-means, 24, 59, 72 fuzzy k-nearest neighbor, 41 fuzzy set, 10, 60, 74, 88 G gain ratio, 598-600 Gaussian RBF kernel, 658 generative tomographic mapping, 699-701 generic routing, 249, 251 genetic algorithm, 10, 36, 39, 48, 50, 82, 193-194, 516-520 genetic k-means, 24, 36 genetic programming, 28, 58 Gini function, 600-601 gray relation analysis, 203-207 H HEp-2 cell patterns, 580 Hessian eigenmaps, 706-707 hidden Markov Model, 60 Hostelling T2 control chart, 421-423 hybrid decision tree, 400-406 hybrid method, 189, 521-528 hyperspectral images, 483-496 I image data, 41, 43, 81, 95, 577 image mining, 577 imbalanced data, see class imbalance information gain, 598-600 information graph, 338-342 information networks, 329-342 Information-Fuzzy Network, 50 instance selection, 11, 104 Subject Index Intelligent Miner, 14, 16 inventory management, 51 ISOMAP, 703-704, K Karhunen-Loève transform, see principal component analysis kernel, 9, 652, 658 k-means, 24, 26, 36, 37, 40, 57 knowledge discovery process, 6-9, 154-171 KnowledgeSEEKER, 61 Kruskal-Wallis test, 399 L Laplacian eigenmaps, 704-706 least squares support vector machines, 657-662 linear kernel, 658 local tangent space alignment, 707-708 locally linear embedding, 701-703 logistic regression, 116-117 LTSA, see local tangent space alignment M maintenance planning, 505 manifold-learning methods, 691-744 manufacturing enterprise system, market segmentation, 24, 36 MineSet, 14, 15, 57 missing value, 7, 8, 88, 395 multi-classification support vector machines, 662-674 multi-dimensional functional data, 463 multi-dimensional scaling, 697-699 multi-objective classification models, 359-361 multi-objective information networks, 336-338 777 N naïve Bayes, 120-121 nearest neighbor, 9, 10, 41, 50, 63, 119-120 neural networks, 9, 10, 16, 25, 26, 27, 29, 37, 41 50, 58, 61, 63, 81, 126-129, 192-193, 383-393 nonparametric multivariate control chart, 413 O OAA, see one-against-all OAO, see one-against-one OLAP, see on-line analytical processing one-against-all, 662-664 one-against-one, 664-665 on-line analytical processing, 9, 23 order batching, 51 P pairwise multi-classification support vector machines, 665-672 partial least squares, 62, 74, 75, 81, 475 PCA, see principal component analysis Petri-net-based workflow models, 549 polynomial kernel, 658 possible rules, 540 principal component analysis, 62, 63, 69, 72, 73, 74, 75, 79, 81, 88, 95, 397, 486, 695-697 process control, 2, 62, 63, 95 process platform formation, 247 production control, 20, 48, 287 Q quality control, 2, 4, 18, 19, 20, 21, 39, 69 778 Recent Advances in Data Mining of Enterprise Data quality improvement, 4, 81, 95 Qualtrend, 15, 16 SVD, see singular value decomposition SVM, see support vector machine R T random forest, 28, 80 regression, 6, 9, 16, 25, 28, 29, 37, 49, 50, 62, 69, 74, 231, 747 ROC curve, 133 rough sets, 10, 39, 40, 69, 79, 81, 95, 508-512 routing clustering, 265-267 routing similarity measure, 251-265 routing unification, 267-275 rule induction, 3, 9, 58, 63, 69, 326 tabu search, 520-521 TAN technique, 121-122 telecommunication, 147 text mining, 95, 247, 254 time series data, 36, 82, 87, 95, 189, 191, 344 tree growing, 269-275 tree matching, 247, 262 tree pruning, 615-618 S sales forecasting, 194-200 scheduling of wafer fabrication, 291-294 self-organizing map, 24, 36, 37, 40, 50, 57, 58, 59 semiconductor manufacturing, 393 sensor positioning, 738-739 sequential forward floating selection, 75, 89 service enterprise system, significance run algorithm, 731 similarity measure, 249 simulation, 287, 290 single-objective information networks, 330-336 singular value decomposition, see principal component analysis soft computing, 10, 16, 93, 189 SOM, see self-organizing map SPC, see statistical process control spectral band selection, 490-494 Statistica Data Miner, 15 statistical process control, 415-419 supervised neural networks, 388-390 supplier selection, 83 support vector machine, 10, 27, 29, 39, 97, 129-131, 495, 646-657 U unsupervised neural networks, 390-393 V visual data mining, 10, 11, 79, 95 voting scheme, 168 W wafer acceptance test data, 396-397 wafer bin map, 59, 87, 88, 369 wafer fabrication, 289 WAT data, see wafer acceptance test data wavelet, 37, 61, 62, 73, 79, 89, 465-468 WBM, see wafer bin map web mining, 15, 91 weighted evolving fuzzy neural network, 218-229 wine quality, 323, 324 winery database, 325, 343 Winter’s method, 207-209 workflow log mining, 557 workflow model, 545, 549-552 workflow optimization, 557 List of Contributors Nikolaos M Avouris Department of Electrical and Computer Engineering University of Patras Petras, Greece Email: avouris@upatras.gr Pei-Chann Chang Department of Information Management Yuan Ze University No 135, Yuan-Tung Rd Chung-li, Tao-Yuan 32026, Taiwan, R.O.C E-mail: iepchang@saturn.yzu.edu.tw Guoqing Chen School of Economics and Management Research Center for Contemporary Management Tsinghua University Beijing 100084, China Email: chengq@em.tsinghua.edu.cn Chen-Fu Chien Department of Industrial Engineering and Engineering Management National Tsing Hua University Hsin Chu, Taiwan Email: cfchien@mx.nthu.edu.tw Sophia Daskalaki Department of Engineering Sciences University of Patras Petra, Greece Email: sdask@upatras.gr 779 780 Recent Advances in Data Mining of Enterprise Data Sigal Elnekave Department of Information Systems Engineering Ben-Gurion University of the Negev Beer-Sheva 84105, Israel E-mail: elnekave@bgu.ac.il C Jack Feng Department of Industrial and Manufacturing Engineering Bradley University Peoria, Illinois 61625, USA Email: cfeng@bradley.edu Dimitrios Gunopulos Department of Computer Science and Engineering University of California at Riverside Riverside, CA, USA Email: dg@cs.ucr.edu Xunhua Guo School of Economics and Management Tsinghua University Beijing 100084, China Shao-Chung Hsu Department of Industrial Engineering and Engineering Management National Tsing Hua University Hsin Chu, Taiwan Xiaoming Huo School of Industrial Engineering Georgia Institute of Technology Atlanta, GA, U.S.A Email: xiaoming@isye.gatech.edu Myong K Jeong Department of Industrial & Information Engineering University of Tennessee Knoxville, TN 37996-0700, USA Email: mjeong@utk.edu List of Contributors Jianxin (Roger) Jiao School of Mechanical and Aerospace Engineering Nanyang Technological University Nanyang Avenue, Singapore 639798 Email: jiao@pmail.ntu.edu.sg L P Khoo School of Mechanical and Aerospace Engineering Nanyang Technological University North Spine (N3) Level 2, 50 Nanyang Avenue, Singapore 639798 Emails: mlpkhoo@ntu.edu.sg Seong G Kong Department of Electrical and Computer Engineering University of Tennessee Knoxville, TN 37996-2100, USA Ioannis Kopanas OTE S.A, Hellenic Telecommunications Organization Patras, Greece Email: ikopanas@ote.gr Andy Koronios School of Computer and Information Science University of South Australia, Australia Mark Last Dept of Information Systems Engineering Ben-Gurion University of the Negev Beer-Sheva 84105, Israel E-mail: mlast@bgu.ac.il T Warren Liao Industrial Engineering Department Louisiana State University CEBA Building, No 3128, Baton Rouge, LA 70803, U.S.A Email: ieliao@lsu.edu H Y Lim Fabristeel Pte Ltd, 9, Tuas Avenue 10 Singapore 639133 781 782 Recent Advances in Data Mining of Enterprise Data Hyeung-Sik Min Sandia National Laboratories Albuquerque, New Mexico, USA Email: hjmin@sandia.gov Amos Naor Golan Research Institute University of Haifa P.O Box 97, Kazrin 12900, Israel E-mail: amosnaor@research.haifa.ac.il Xuelei (Sherry) Ni Department of Mathematics and Statistics Kennesaw State University Kennesaw, GA, USA Email: xni2@kennesaw.edu Olutayo O Oladunni Department of Engineering Education Purdue University West Lafayette, IN, USA Olufemi A Omitaomu Department of Industrial & Information Engineering University of Tennessee Knoxville, TN 37996-0700, USA Petra Perner Institute of Computer Vision and Applied Computer Sciences, IBaI Leipzig, Germany Email: www.ibai-institut.de Giovanni C Porzio Department of Economics University of Cassino Via S.Angelo I-03043 Cassino (FR), Italy Email: porzio@eco.unicas.it Giancarlo Ragozini Department of Sociology Federico II University of Naples Vico Monte di Pietà 1, I-80132 Naples, Italy Email: giragoz@unina.it List of Contributors Victor Schoenfeld Yarden - Golan Heights Winery Katzrin, Israel E-mail: victor@golanwines.co.il Andrew K Smith School of Industrial Engineering Georgia Institute of Technology Atlanta, GA, USA Sharmila Subramaniam Google Inc Mountain View, CA, USA Email: sharmi@cs.ucr.edu Theodore B Trafalis School of Industrial Engineering University of Oklahoma Norman, OK, USA Email: ttrafalis@ok.edu Yen-Wen Wang Department of Industrial Engineering and Management Ching-Yun University No 229 Chien-Hsin Rd Taoyuan 320, Taiwan E-mail: ywwang@cyu.edu.tw Yuehwern Yih School of Industrial Engineering Purdue University West Lafayette, Indiana, USA Email: yih@purdue.edu Lan Yu Department of Computer Science and Technology Tsinghua University Beijing 100084, China Lianfeng Zhang School of Mechanical and Aerospace Engineering Nanyang Technological University Nanyang Avenue, Singapore 639798 783 ... the recent advances in data mining and knowledge discovery of xxiii xxiv Recent Advances in Data Mining of Enterprise Data enterprise data, with focus on both algorithms and applications The intended... Defining the data mining function and performance measures 3.6 Selection of data mining algorithms 3.7 Experimentation with data mining algorithms 3.8 Combining classifiers and interpretation of. .. Data mining algorithms/ methodologies 2.3 Data mining system architectures 2.4 Data mining software programs Types and Characteristics of Enterprise Data Overview of the Enterprise Data Mining
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Xem thêm: IT training recent advances in data mining of enterprise data algorithms and applications liao triantaphyllou 2008 01 15 , IT training recent advances in data mining of enterprise data algorithms and applications liao triantaphyllou 2008 01 15 , Operationalization of the Discovered Knowledge: Design of an Intelligent Insolvencies Management System, 3 Hybridizing a genetic algorithm (GA) with a neural network for forecasting, A Case Study: the Cabernet Sauvignon problem, Designing Nonparametric Charts When Large HDS Are Available: the Data Depth Approach, 4 A case study: data mining in hyperspectral imaging, Rough Sets, Genetic Algorithms, and Tabu Search, 2 Group 2: Semi-classical methods: multidimensional scaling (MDS), 4 Group 4: Methods from spectral theory, 5 Group 5: Methods based on global alignment

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