DATA MINING AND KNOWLEDGE DISCOVERY APPROACHES BASED ON RULE INDUCTION TECHNIQUES DATA MINING AND KNOWLEDGE DISCOVERY APPROACHES BASED ON RULE INDUCTION TECHNIQUES Edited by EVANGELOS TRIANTAPHYLLOU Louisiana State University, Baton Rouge, Louisiana, USA GIOVANNI FELICI Consiglio Nazionale delle Ricerche, Rome, Italy ^ Spri ringer Library of Congress Control Number: 2006925174 ISBN-10: 0-387-34294-X e-ISBN: 0-387-34296-6 ISBN-13: 978-0-387-34294-8 Printed on acid-free paper © 2006 Springer Science-i-Business Media, LLC All rights reserved This work may not be translated or copied in whole or in part without the written permission of the publisher (Springer Science-fBusiness Media, LLC, 233 Spring Street, New York, NY 10013, USA), except for brief excerpts in connection with reviews or scholarly analysis Use in connection with any form of information storage and retrieval, electronic adaptation, computer software, or by similar or dissimilar methodology now known or hereafter developed is forbidden The use in this publication of trade names, trademarks, service marks, and similar terms, even if they are not identified as such, is not to be taken as an expression of opinion as to whether or not they are subject to proprietary rights Printed in the United States of America 987654321 springer.com I gratefully dedicate this book to my new life's inspiration, my mother Helen and late father John (loannis), my late Grandfather (Evangelos), and also to my beloved Ragus and Ollopa ("Ikasinilab") It would had never been prepared without their encouragement, patience, and unique inspiration —Evangelos Triantaphyllou I wish to dedicate this book to la Didda, le Pullalle, and Misty—four special girls who are always on my side—and to all my friends, who make me strong; to them goes my gratitude for their warm support —Giovanni Felici TABLE OF CONTENTS List of Figures List of Tables Foreword Preface Acknowledgements xxiii xxix xxxvii xxxix xlvii Chapter A COMMON LOGIC APPROACH TO DATA MINING AND PATTERN RECOGNITION, by A Zakrevskij Introduction 1.1 Using Decision Functions 1.2 Characteristic Features of the New Approach Data and Knowledge 2.1 General Definitions 2.2 Data and Knowledge Representation the Case of Boolean Attributes 2.3 Data and Knowledge Representation the Case of Multi-Valued Attributes Data Mining - Inductive Inference 3.1 Extracting Knowledge from the Boolean Space of Attributes 3.2 The Screening Effect 3.3 Inductive Inference from Partial Data 3.4 The Case of Multi-Valued Attributes Knowledge Analysis and Transformations 4.1 Testing for Consistency 4.2 Simplification Pattern Recognition - Deductive Inference 5.1 Recognition in the Boolean Space 5.2 Appreciating the Asymmetry in Implicative Regularities 5.3 Deductive Inference in Finite Predicates 5.4 Pattern Recognition in the Space of Multi-Valued Attributes Some Applications Conclusions References Author's Biographical Statement 2 6 10 12 12 18 20 21 23 23 27 28 28 31 34 36 38 40 41 43 Chapter THE ONE CLAUSE AT A TIME (OCAT) APPROACH TO DATA MINING AND KNOWLEDGE DISCOVERY, by E Triantaphyllou 45 viii Data Mining & Knowledge Discovery Based on Rule Induction 4.1 4.2 4.3 4.4 4.5 6.1 6.2 6.3 46 49 52 54 54 58 59 62 65 70 72 72 74 Introduction Some Background Information Definitions and Terminology The One Clause at a Time (OCAT) Approach Data Binarization The One Clause at a Time (OCAT) Concept A Branch-and-Bound Approach for Inferring Clauses Inference of the Clauses for the Illustrative Example A Polynomial Time Heuristic for Inferring Clauses A Guided Learning Approach The Rejectability Graph of Two Collections of Examples The Definition of the Rej ectability Graph Properties of the Rejectability Graph On the Minimum Clique Cover of the Rej ectability Graph Problem Decomposition 7.1 Connected Components 7.2 Clique Cover An Example of Using the Rejectability Graph Conclusions References Author's Biographical Statement 76 77 77 78 79 82 83 87 Chapter AN INCREMENTAL LEARNING ALGORITHM FOR INFERRING LOGICAL RULES FROM EXAMPLES IN THE FRAMEWORK OF THE COMMON REASONING PROCESS, by X Naidenova Introduction A Model of Rule-Based Logical Inference 2.1 Rules Acquired from Experts or Rules of the First Type 2.2 Structure of the Knowledge Base 2.3 Reasoning Operations for Using Logical Rules of the First Type 2.4 An Example of the Reasoning Process Inductive Inference of Implicative Rules From Examples 3.1 The Concept of a Good Classification Test 3.2 The Characterization of Classification Tests 3.3 An Approach for Constructing Good Irredundant Tests 3.4 Structure of Data for Inferring Good Diagnostic Tests 3.5 The Duality of Good Diagnostic Tests 3.6 Generation of Dual Objects with the Use 89 90 96 97 98 100 102 103 103 105 106 107 109 Table of Contents of Lattice Operations Inductive Rules for Constructing Elements of a Dual Lattice Special Reasoning Operations for Constructing Elements of a Dual Lattice 3.8.1 The Generalization Rule 3.8.2 The Diagnostic Rule 3.8.3 The Concept of an Essential Example Algorithms for Constructing All Good Maximally Redundant Tests 4.1 NIAGaRa: A Non-Incremental Algorithm for Constructing All Good Maximally Redundant Tests 4.2 Decomposition of Inferring Good Classification Tests into Subtasks 4.2.1 Forming the Subtasks 4.2.2 Reducing the Subtasks 4.2.3 Choosing Examples and Values for the Formation of Subtasks 4.2.4 An Approach for Incremental Algorithms 4.3 DIAGaRa: An Algorithm for Inferring All GMRTs with the Decomposition into Subtasks of the First Kind 4.3.1 The Basic Recursive Algorithm for Solving a Subtask Ofthe First Kind 4.3.2 An Approach for Forming the Set STGOOD 4.3.3 The Estimation of the Number of Subtasks to Be Solved 4.3.4 CASCADE: Incrementally Inferring GMRTs Based on the Procedure DIAGaRa 4.4 INGOMAR: An Incremental Algorithm for Inferring All GMRTs Conclusions Acknowledgments Appendix References Author's Biographical Statement 3.7 3.8 ix 110 111 112 112 113 114 115 115 122 123 125 127 129 130 130 131 131 132 132 138 138 139 143 147 Chapter DISCOVERING RULES THAT GOVERN MONOTONE PHENOMENA, by V.I Torvik and E Triantaphyllou Introduction Background Information 2.1 Problem Descriptions 2.2 Hierarchical Decomposition of Variables 2.3 Some Key Properties of Monotone Boolean Functions 149 150 152 152 155 157 X Data Mining & Knowledge Discovery Based on Rule Induction 2.4 Existing Approaches to Problem 2.5 An Existing Approach to Problem 2.6 Existing Approaches to Problem 2.7 Stochastic Models for Problem 3 Inference Objectives and Methodology 3.1 The Inference Objective for Problem 3.2 The Inference Objective for Problem 3.3 The Inference Objective for Problem 3.4 Incremental Updates for the Fixed Misclassification Probability Model 3.5 Selection Criteria for Problem 3.6 Selection Criteria for Problems 2.1,2.2, and 2.3 3.7 Selection Criterion for Problem Experimental Results 4.1 Experimental Results for Problem 4.2 Experimental Results for Problem 4.3 Experimental Results for Problem Summary and Discussion 5.1 Summary of the Research Findings 5.2 Significance of the Research Findings 5.3 Future Research Directions Concluding Remarks References Authors' Biographical Statements 160 162 162 162 165 165 166 166 167 167 168 169 174 174 176 179 183 183 186 187 187 188 191 Chapter LEARNING LOGIC FORMULAS AND RELATED ERROR DISTRIBUTIONS, by G Felici, F Sun, and K Truemper Introduction Logic Data and Separating Set 2.1 Logic Data 2.2 Separating Set Problem Formulation 3.1 Logic Variables 3.2 Separation Condition for Records in ^ 3.3 Separation Condition for Records in 3.4 Selecting a Largest Subset 3.5 Selecting a Separating Vector 3.6 Simplification for 0/1 Records Implementation of Solution Algorithm Leibniz System Simple-Minded Control of Classification Errors 193 194 197 197 198 200 201 201 201 202 203 204 204 205 206 Table of Contents Separations for Voting Process Probability Distribution of Vote-Total 8.1 Mean and Variance for Z ^ 8.2 Random Variables Yi 8.3 Distribution for 8.4 Distribution for Z ^ 8.5 Probabilities of Classification Errors 8.6 Summary of Algorithm Computational Results 9.1 Breast Cancer Diagnosis 9.2 Australian Credit Card 9.3 Congressional Voting 9.4 Diabetes Diagnosis 9.5 Heart Disease Diagnosis 9.6 Boston Housing 10 Conclusions References Authors' Biographical Statements xi 207 208 209 211 212 213 213 216 216 218 219 219 219 220 221 221 222 226 Chapter FEATURE SELECTION FOR DATA MINING by V de Angelis, G Felici, and G Mancinelli Introduction The Many Routes to Feature Selection 2.1 Filter Methods 2.2 Wrapper Methods Feature Selection as a Subgraph Selection Problem Basic IP Formulation and Variants Computational Experience 5.1 Test on Generated Data 5.2 An Application Conclusions References Authors' Biographical Statements 227 228 229 232 234 237 238 241 242 246 248 249 252 Chapter TRANSFORMATION OF RATIONAL AND SET DATA TO LOGIC DATA, by S Bartnikowski, M Cranberry, J Mugan, and K Truemper Introduction 1.1 Transformation of Set Data 1.2 Transformation of Rational Data 253 254 254 254 Author Index Lemmer, J.F., 371 Leung, K.S., 398 Levchenko, V.I., 38 Lewis, D., 702 Li,R.P.,519, 532 Liao, S.Y., 461 Liao, T.W., 495, 498, 503, 535 Lichko, A.E., 97 Likens, D.A., 525, 534, 536 Lim,T.S.,319,320 Lin, B., 524, 533 Lin, C.T., 521,536 Lin, R., 573, 585 Lin, T.Y., 376 Lin,Y., 505, 519,520,523, 532, 533,534,536 Lin, Z.L, 468, 469 Lindell, Y , 462 Linoff, G., 298 Litz,L., 516, 533,535 Liu, D., 469 Liu, H., 228, 233 Liu, W.Z., 332 Llora, X , 447,448 Lo, W., 91 Logan, R., 371 Loh,W.Y.,319 Loncoln, J.E., 632, 636, 638, 639, 647 Long, J.S., 669 Longemann, G., 24 Lu, H., 484 Lu, Y.C., 520, 533, 536 Luba, T., 368 Lucchetti, D., 588 Luo, J.X., 485 M Madria, S.K., 697, 698 Mahfoud, S., 443 Maimon, O., 461 Maitre, H., 582, 583 733 Majumdar, S., 376 Makino,K., 161, 164,175,183, 194, 661 Maloof, M.A., 90 Mamdani, E.H., 496 Mancinelli, G., 227 Manderick, B., 444 Mandl,K.D., 559,561,563 Mangasarian, O.L., 51, 64, 194, 218,307,313,317,329,434 Mangu, L., 600 Mannila,H.,95,461,466 Mansilla, E.B., 447 Mansour, Y., 49 Markwell, D.C., 563 Martin, J.K, 332 Martin, J.H., 600 Martin, W.N., 444 Martin-Bautista, M.J., 629, 631, 641,643,646 Martinez, T., 92 Masand, B., 631 Matheus, C.J., Mayoraz, E., 661 Mazlack, L.J., 468 McCluskey, E., 50 McCluskey, E.J., 27 McCormick, E., 513, 533 McCullagh, P., 63 McKay, R.L, 433, 447, 453 McLachlan, G.J., 668 Megalooikonomou, K., 588 Megretskaya, A., 95 Mekaoche, A., 447 Mendel,J.M., 501,533, 534 Merz, C.J., 449 Meyer, M., 690 Michalewicz, Z., 400, 402 Michalski, R.S., 61, 90, 94, 113, 114,286,364,427 Mikhailov, L., 505 Milanova, M.G., 570 Mille,J.S.,91,94, 104, 138 Miller, R.J., 258 734 Data Mining & Knowledge Discovery Based on Rule Induction Mingers, J., 365 Misiti, M., 570 Mitchell, J.S., 384 Mitchell, M , 400, 402, 436,437 Mitchell, T., 257, 260 Mitra,S., 462, 518,533 Miyakawa, M., 331 Miyamoto, S., 634 Mobasher, B., 698 Molthen, R.C., 584, 585 Monk, D.L., 632, 638, 639, 647 Monties, S., 462 Moore, A.W., 236, 237 Moore, R.A., 664 Mori, S., 703 Morik, K., 362 Morimoto,Y., 461,474 Motoda, H., 228 Motwani, R., 69, 460, 698 Moulinier, I., 703 Muchnik, I., 661 Mueller, A., 461 Mueller, R.K., 27 Mugan, J., 253 Muggleton, S., 194 Muhlenbein, H., 444 Murphy, P.M., 332, 334, 449 Murthy, S.K., 220, 306, 317, 319, 329,331,332,335 Nakashima, T., 465, 532 Nanck, D., 462 Nanda, S., 376 Nasraoui, O., 631 Nauck,D.,519,520, 533 Nawa,N.E., 516, 532,535 Nelder, J.A., 663 Nelson, M.M., 194 Nelson, R.L., 27 Newton, C , 435 Newton, S.L., 370, 373 Niehaus, W., 77 Nieto Sanchez, S., 71, 82,90, 186 Nilsson, N.J., 24 Noda,E.,395,405,411 Nordin, P., 447 Nowicki, R., 377 Nozaki, K., 504 o Oliveira, A.L., 233, 334 Olson, J., 290 Omiecinski, S., 461 Ore,0., 108 Orsenigo, C , 305, 307 P Pal, S.K., 462 Papagelis, A., 403 Pappas, N.L., 50 Pardalos, P.M., 77, 78 Pattipati,K.R., 331,332 Pawlak, Z., 282, 287, 300, 360, 361,367,374,375,376, 378 Pazzani, M.J., 258, 329, 333, 334, 236,631 Pearl, J., 373 Pedersen, T., 623 Pedersen,J.P., 701,702 Pedrycz, W., 469, 497, 507 Pena-Reyes, C.A., 517, 532 Perlson, A.S., 443 Pemer, P., 588, 589 Peysakh, J., 53 Pezzoli, A., 697 Pham,D.T.,364,366,381 Piaget, J., 94 Piatesky-Shapiro, G., 4, 7, 12, 460,474, 700 Picard, J.C, 164 Pitt, L., 49 Author Index Pivert, 0.,461,465 Poeck, K., 96 Polegaeva, J.G., 94, 95, 96 Ponce, J., 265 Porter, S.C, 559, 580 Pospelov, D.A., 2, Potharst, R., 334, 335 Powers, D., 600 Prade, H., 372, 374, 375, 376 Prather, J.C, 552, 556, 564, 568 Pregibon,, 368 Preparata, F.P., 293 Priestly, H.A., 657 Provost, F.J., 399, 700 Q Quine, W.V., 27 Quinlan, J.R., 51, 136, 219, 221, 235,258,282,316,317,319, 329,330,331,332,335,360, 364,365,366,368,369,381, 409, 447, 527 R Ragsdale, C.T., 434 Raiha, K.J., 95 Ram, A., 94, 701 Ramakrishnan, K.G., 50, 260 Rao, M.M, 370 Rasiova, H., 131 Rasmussen, E., 634 Rastogi, R., 461 Rauszer, C , 374 Ravi, V , 505, 532, 533 Ravishankar, C.S., 330, 332 Rentala, C.C, 77, 78 Resende, M.G.C., 50, 64, 69 Richards, D., 444 Rickard, H.E., 565 Rifqi, M , 462 Riguet, J., 108 735 Ringuette, M., 702 Riolo,R.L.,435,439,440 Ripley, B.D., 283 Risannen, J., 331 Rivest,R.L., 49, 136,329,331 Robb, R.A., 582 Roberto, J., 474 Robertson,!., 164 Rocchio, J., 702 Rogot, E.„ 690 Rojas-Guzman, C , 386 Romahi, Y., 485 Romanov, V.I., 38 Romanyuk, A., 48 Roth, D., 598,600,601,612 Roychowdhury, S., 469 Rubin, D.B, 672, 674, 690 Rubinfeld, D.L., 221 Rushing, J.A., 576, 579 Russo,M, 514, 532,533, 534 Rutkowska, D., 524, 533 Sahami, M., 233, 258 Salamon, P., 447 SaIton,G., 631,633, 634 Salter, F., 461 Salzberg, S.,92,329,331,332, 335,338 Sanchez, D., 460, 485 Sarker, R., 435 Sarvarayudu, G.P.R., 330 Savasere, E., 461 Savnik, I., 461 Scgreider, M., 476 Schabes, Y., 598, 600 Schaffer, C , 403 Scheuer, T., 505 Scheuren, F., 690 Schlimmer, J.C, 233 Selenschtschitow, A., 515, 532, 533,534,535 736 Data Mining & Knowledge Discovery Based on Rule Induction Selker,H.P, 581 Seltser, M., 238 Sethi, I.K., 330 Setiono, R., 233 Shafer, G., 360, 371 Shafer,J.C.,461 Shamos, M., 1.293 Shankar, P.M., 584, 585, 586 Shannon, C.E., 330, 332 Sharkey, A.C.J., 447 Shavlik, J.W., 48, 51, 194, 220, 235 Shaw, M.J., 529, 530 Sheinvald, J., 233 Shen, Q., 464, 483,485 Shi,Y.,498, 515, 532, 535 Shih,Y.S.,319 Shim, K., 461 Shipp, C.A., 447 Shiu, C , 485 Shlien, S., 332 Shmulevich, I., 150 Shortliffe, E.H., 369, 556 Shragai, A., 476 Shreider, J., 94 Shu, J.Y., 465, 469, 478, 479 Silverman, B.W., 150, 164, 331 Singer, Y., 702 Singhal, M., 483 Sipper,M., 517, 532 Skalak, D.B., 236 Skowron, A., 374 Sloan, R.H., 51 Slowinski K., 377 Slowinski, R., 376, 374,376 Smith, A.F.M., 672 Smith, J.E., 701 Smith, J.W., 220 Smith, R.E., 435, 443 Smith, S.F., 406, 440 Smolikova, R., 583, 584, 585 Smyth, P., 415, 700 Sokolov, N.A., 157, 160, 165, 184 Solaiman,B., 581,582, 584 Sorlie, P.D., 690 Soyster, A.L., 53, 64, 70, 71, 75, 76,77, 186,194,206 Spencer, J.H., Spemer,E., 109 Spiegelhalter, D.J., 397, 672 Spiessens, P., 444 Spiliopoulou, M., 631 Srikant, R., 258, 460, 461, 463, 466, 467, 470, 474, 475, 484 Stefanowski, J., 374, 376 Steinbach, M., 702 Stone, P.J., 282 St-Onge, D., 600 Su, M.C., 522 Sugeno, M., 496, 506, 507, 532, 533,534 Sun, F., 193 Sun, R., 48 Sunter, A.B., 659, 660 Surmann, H., 515, 532, 533, 534, 535 Sutiwaraphun, J., 707 Suzuki, E., 474 Swarmi, A., 461 Syrbu, V.N., 96 Syswerda, G., 401 T Talia, D., 708 Tan, A-H., 701 Tanese, R., 444, 445 Tang, P.C, 552, 557, 559, 560, 580 Tamg,Y.S.,513, 533 Tatsuoka, C , 162 Taylor, P.C, 331 Tecuci, G., 360 Teuhola, J., 477 ThayseA., Thibaudeau, Y., 658 Thearling, K., 703, 705 Author Index Thiele, L., 448 Thiesson, B., 368 Thomas, A., 672 Thomas, J.A., 407 Thompson, D.J., 174 Thrun, S.B., 194 Toivonen, H., 461 Tong, S., 570 Torri,Y,51 Torvik, V.I., 52, 82, 149, 150,163, 167, 168, 186, 188 Tou, J.T., 283 Tourassi, G.D., 565 Towell, G., 48 Triantaphyllou, E., 2, 45, 51, 52, 53,59,60,61,62,64,65,68, 70,71,75,76,77,82, 149, 163, 167, 168, 186, 188, 194, 195,206,360,364,461,469, 661,698 Truemper, K., 51, 186, 193, 197, 205,218,247,253,256,259, 260, 368, 597, 603, 608, 619 Trunfio, P., 708 Tsang, E.C.C., 465, 469, 478, 479,529,533 Tseng, S.M., 461 Tukey, J.W., 703 Turner, K., 447 Tung, W.L., 533, 534 Tumey, P., 697 Umanol, M., 530 U Uthurusamy, R., 366, A6\A(>^ Van De Merckt, T., 330 Van der Meer, O., 334, 335 Vandewalle, J., 508, 532, 534 Van Mechelen, I., 656 737 Vapnik, V., 309 Varshney, P.K., 330 Vasilkova, I.V., 20, 38 Venables, W.N., 283 Vercellis, C , 305,307 Vigario, R., 570 Vila, M., 629 Vincentelli, A.S., 334 Vincitelli, A.S., 233 Vityaev,E., 150 Vu, D.T., 95 Vu,Q.,461,475 W Wang, C.H., 533 Wang, K., 258 Wang, L.X., 501, 533, 534 Wang, S.L., 461, 464, 483 Wang, X., 528 Wang, X.Z., 527, 529, 530, 533 Ward,M., 159 Warmuth, M., 49 Waterman, D., Wehenkel,L., 531,534, 536 Wei, Q., 459,460, 461, 462, 464, 465,471,472,473,474,475, 480,481,482,483,485 Weiss, G.M., 399 Weld, D., 697 Whitaker, C.A, 447 White, P., 332 Whitley, D , 444 Wickens, G., 290 Wiebe, J., 623 Wiedeman,D., 159 Wiener, E., 702 Wilcox, A , 558, 573, 574 Wille, R.W., 95, 114 Wilk, M.B., 703 Wilson, S.W., 292, 440 Winograd, T., 698 Winkler, W.E., 668, 690 738 Data Mining & Knowledge Discovery Based on Rule Induction Witten, H., 328, 329, 347 Wnek, J., 286 Wolberg,W.H., 51, 194,218 Wolpert, D.H., 207 Wong, C , 485 Wong, M.L., 398 Wong, P-C, 703 Wong, S.K.M., 360, 369, 376, 382 Wu, T.P., 533, 527 Wu,X.,91,258, 533, 528 Wu, ,468 Wygralak, W., 374, 376 X Xue, J., 77 Yancey, W.E., 679 Yang, J., 286 Yang, Y., 258, 525, 701,702 Yang, Y.P., 483 Yao, J., 508, 532 Yao, X., 442 Yasukawa, T , 506, 507, 532, 533, 534 Ye, Y., 238 Yeh,M.S., 531 Yeung, D.S., 530 Yilmaz, E., 82, 186,461 Yuan, B., 634 Yuan, Y., 529, 530 Zakrevskij, A.D., 1, 2, 3, 4, 5, 10, 11, 14,15,20,21,24,27,28, 34, 36, 38, 39 Zakrevsky, A.D., see Zakrevskij, A.D Zeng, L.„ 690 Zhai, L.Y., 359, 366, 388 Zhang, H., 24 Zhang, J., 238, 505, 532 Zhang, Y.Q., 522, 533 Zhao, Y., 608, 619 Zhou, R.W., 523, 534, 536 Zhuang, V., 533 Ziarko, W., 363, 376, 377 Zimmermann, H.J., 373 Zitzler, E., 448 Zurita, J.M., 527, 533 Zhu, P., 698 Zhu, S., 706 LIST OF CONTRIBUTORS Abbass, Hussein A Artificial Life and Adaptive Robotics Lab School of Information Technology & Electrical Engineering University of New South Wales at ADFA Canberra, ACT 2600 AUSTRALIA Email: h.abbass@adfa.edu.au Web: http://www.itee.adfa.edu.au/~abbass Al-Mubaid, Hisham University of Houston at Clear Lake Department of Computer Science Clear Lake, TX USA Email: hisham@cl.uh.edu Bartnikowski, Stephen Department of Computer Science ECS University of Texas at Dallas Box 830688 Richardson, TX 75083-0688 U.S.A Email: sibart@utdallas.edu Chen, Guoqing School of Economics and Management Tsinghua University Beijing 100084, CHINA Email: chengq@em.tsinghua.edu.cn Chen, Jianhua Department of Computer Science Louisiana State University Baton Rouge, LA 70803-4020 U.S.A Email: iianhua@bit.csc.lsu.edu 740 Data Mining & Knowledge Discovery Based on Rule Induction De Angelis, Vanda Facolta di Scienze Statistiche Universita degli studi di Roma "La Sapienza" Piazzale A Moro 00185 Rome ITALY Email: vanda.deangeli$@uniromal.it Elmaghraby, Abel S University of Louisville Department of Computer Engineering and Computer Science J.B Speed Scientific School Louisville, KY 40292, U.S.A Felici, Giovanni Istituto di Analisi dei Sistemi ed Informatica Consiglio Nazionale delle Ricerche Viale Manzoni 30 00185 Rome ITALY Email: felici@iasi.cnr.it Fok, Sai-Cheong Faculty of Engineering & Surveying University of Southern Queensland Toowoomba, Qld 4350, AUSTRALIA Email: foksai@usq.edu.au Web: http://www.usq.edu.au/users/foksai/ Freitas, Alex A Computing Laboratory, University of Kent Canterbury Kent, CT2 7NF UK Email: A.A.Freitas@kent.ac.uk Web: http://www.cs.kent.ac.uk/people/staff/aaf Cranberry, Matthias Department of Computer Science EC31 University of Texas at Dallas Box 830688 Richardson, TX 75083-0688 U.S.A Email: matthias@utdallas.edu List of Contributors Judson, Dean H U.S Census Bureau 4700 Silver Hill Road Suitland, MD 20746 U.S.A Email: Dean.H.Judson@census.gov Kantardzic, Mehmet M University of Louisville Department of Computer Engineering and Computer Science J.B Speed Scientific School Louisville, KY 40292, U.S.A Email: mmkantOl @athena.louisville.edu Kerre, Etienne E Department of Applied Mathematics and Computer Sciences University of Gent Krilgslaan281/S9 9000 Gent BELGIUM Email: eekerre@gent.edu.be Khoo, Li-Pheng School of Mechanical & Production Engineering Nanyang Technological University 50 Nanyang Avenue SINGAPORE 639798 Email: mlvzhai@ntu.edu.sg, mlpkhoo@ntu.edu.sg Web: http://www.ntu.edu.sg/mpe/Admin Kirley, Michael School of Environmental & Information Sciences Charles Stuart University Thurgoona Campus, PO Box 789 Albury, NSW2640 AUSTRALIA Email: mkirlev@unimelb.edu.au Kraft, Donald H Department of Computer Science Louisiana State University Baton Rouge, LA 70803-4020 U.S.A Email: kraft@bit.csc.lsu.edu 741 742 Data Mining & Knowledge Discovery Based on Rule Induction Kusiak, Andrew Intelligent Systems Laboratory Mechanical and Industrial Engineering 2139 Seamans Center The University of Iowa Iowa City, Iowa 52242 - 1527 U.S.A Email: andrew-kusiak@uiowa.edu Web: http://www.icaen.uiowa.edu/-ankusiak Lee, Jun-Youl Iowa State University 2019 Black Engineering Ames, lA 50010, U.S.A Liao, T Warren Department of Industrial & Manufacturing Systems Engineering 3128 CEBA Building Louisiana State University Baton Rouge, LA 70803 U.S.A Email: ieliao@lsu.du Web: http://www.imse.lsu.edu/liao Mancinelli, Gabriella Facolta di Scienze Statistiche Universita degli studi di Roma "La Sapienza" Piazzale A Moro 00185 Rome, ITALY Martin-Bautista, Maria J University of Granada Granada 18071 SPAIN Email: mbautis@decsai.ugr.es McKay, Robert (Bob) L School of Information Technology & Electrical Engineering University of New South Wales at ADFA Canberra, ACT 2600 AUSTRALIA Email: rim@cs.adfa.edu.au Web: http://www.itee.adfa.edu.au/-rim List of Contributors Mugan, Jonathan Department of Computer Science EC31 University of Texas at Dallas Box 830688 Richardson, TX 75083-0688 U.S.A Email: iwmO 16000@utdallas.edu Naidenova, Xenia Military Medical Academy Saint Petersburg 196046 Lebedev Street, RUSSIA Email: naidenova@mail.spbnit.ru Noda, Edgar School of Electrical & Comp Eng.(FEEC) State University of Campinas (UNICAMP) Campinas -SP BRAZIL Email: edgar@dt.fee.unicamp.br Olafsson, Sigurdur Iowa State University 2019 B lack Engineering Ames, lA 50010, U.S.A Email: olafsson@iastate.edu Web: http://www.public.iastate.edu/-olafsson Orsenigo, Carlotta Politecnico di Milano P.za Leonardo da Vinci 32 120133 Milano ITALY Email: carlotta.orsenigo@polimi.it Web: http://www.dep.polimi.it/eng/comunita/cl.php?id=70 Sun, Pushing Department of Computer Science Ball State University Muncie, IN 47306 U.S.A Email: fsun@cs.bsu.edu 743 744 Data Mining & Knowledge Discovery Based on Rule Induction Torvik, Vetle L University of Illinois at Chicago Department of Psychiatry MC912, 1601 W Taylor St Chicago, IL 60612 U.S.A Email: vtorvik@uic.edu Web: http://arrowsmith2.psvch.uic.edu/torvik Triantaphyllou, Evangelos Department of Computer Science 298 Coates Hall Louisiana State University Baton Rouge, Louisiana 70803 U.S.A Email: trianta@lsu.edu Web: http://www.csc.lsu.edu/trianta Truemper, Klaus Department of Computer Science ECS University of Texas at Dallas Box 830688 Richardson, TX 75083-0688 U.S.A Email: truemper@utdallas.edu Vercellis, Carlo Politecnico di Milano P.za Leonardo da Vinci 32 120133 Milano ITALY Email: carlo.vercellis@polimi.it Web: http://www.dep.polimi.it/eng/comunita/cLphp?id=70 Vila, Maria-Amparo University of Granada Granada 18071, SPAIN Email: vila@decsai.ugr.es Wachowiak, Mark P University of Louisville Department of Computer Engineering and Computer Science J.B Speed Scientific School Louisville, KY 40292, U.S.A List of Contributors Wei, Qiang School of Economics and Management Tsinghua University Beijing 100084 CHINA Wang, Xiaoting Department of Industrial & Manufacturing Systems Engineering 3128 CEBA Building College of Engineering Louisiana State University Baton Rouge, Louisiana 70803 U.S.A Email: xwang8@lsu.edu Web: http://www.csc.lsu.edu/-xiaoting Zakrevskij, Arkadij United Institute of Informatics Problems of the National Academy of Sciences of Belarus Surganova Str 220012 Minsk BELARUS E-mail: zakr@newman.bas-net.bv Zhai, Lian-Yin School of Mechanical & Production Engineering Nanyang Technological University 50 Nanyang Avenue SINGAPORE 639798 Email: mlYzhai@ntu.edu.sg, mlpkhoo@ntu.edu.sg Web: http://www.ntu.edu.sg/rape/Aclmin Zhu, Peng Department of Industrial & Manufacturing Systems Engineering 3128 CEBA Building College of Engineering Louisiana State University Baton Rouge, Louisiana 70803 U.S.A Email: pzhul@lsu.edu 745 ABOUT THE EDITORS Dr Triantaphyllou did his graduate studies at Penn State University from 1984 to 1990 While at Penn State, he earned a Dual M.S degree in Environment and Operations Research (OR), an M.S degree in Computer Science and a Dual Ph.D degree in Lidustrial Engineering and Operations Research Since the spring of 2005 he is a Professor in the Computer Science Department at the Louisiana State University (LSU) in Baton Rouge, LA, U.S.A., after he has served for 11 years as an Assistant, Associate, and Full Professor in the Industrial Engineering Department at the same university He has also served for one year as an Interim Associate Dean for the College of Engineering at LSU His research is focused on decision-making theory and applications, data mining and knowledge discovery, and the interface of operations research and computer science Since the years he was a graduate student, he has developed new methods for data mining and knowledge discovery and also has explored some of the most fiindamental and intriguing subjects in decision making In 1999 he has received the prestigious IDE (Institute of Industrial Engineers), OR Division, Research Award for his research contributions in the above fields In 2005 he received an LSU Distinguished Faculty Award as recognition of his research, teaching, and service accomplishments Some of his graduate students have also received awards and distinctions including the Best Dissertation Award at LSU for Science, Engineering and Technology for the year 2003 In 2000 Dr Triantaphyllou published a bestseller book on multi-criteria decision-making Also, in 2006 he published a monograph on data mining and knowledge discovery, besides co-editing a book on the same subject He always enjoys sharing the results of his research with his students and is also getting them actively involved in his research activities He has received teaching awards and distinctions His research has been fiinded by federal and state agencies, and the private sector He has extensively published in some of the top refereed journals and made numerous presentations in national and international conferences Dr Triantaphyllou has a strong inter-disciplinary background He has always enjoyed organizing multi-disciplinary teams of researchers and practitioners with complementary expertise These groups try to comprehensively attack some of the most urgent problems in the sciences and engineering He is a strong believer of the premise that the next round of major scientific and engineering discoveries will come from the work of such inter-disciplinary groups More details of his work can be found in his web site (http://www.csc.lsu.edu/trianta/ ) 748 Data Mining & Knowledge Discovery Based on Rule Induction Dr Giovanni Felici graduated in Statistics at the University of Rome "La Sapienza" in 1991 While completing his graduate studies, he won an Erasmus scholarship to attend the Master of Science in Operations Research and Operations Management course at the University of Lancaster, UK, where he was awarded the M.Sc title in 1990 He was then enrolled in the Ph.D program in Operations Research at the University of Rome "La Sapienza", where he successfully defended his dissertation on Classification and Recognition algorithms in 1995 He is presently a permanent researcher in L\SI, the Istituto di Analisi dei Sistemi ed Informatica of the Italian National Research Council (CNR), where he started his research activity in 1994 working on research projects in Logic Programming and Mathematical Optimization, both publicly and privately funded In 1995 and 1996 he was research assistant of Professor Klaus Truemper at the University of Texas at Dallas, TX, where he developed new algorithmic approaches for Data Mining in logic settings His current research activity is mainly devoted to the application of Optimization Techniques to Data Mining problems, with particular focus on Integer Programming algorithms for Learning in Logic and Expert Systems, but he is also active in the field of Operations Research techniques for largescale logistics and production problems; he published papers in international refereed journals and edited books and presented his researches in many international conferences He is also involved in education and teaching, holding graduate and postgraduate courses in Operations Research and Data Mining in two Roman universities and hosting and tutoring students in the Optimization Laboratory for Data Mining (OLDAM), that he co-founded in lASI with some colleagues in 2002 He is one of the members of the board of the Italian Operations Research Association, playing ah active role in the organization of scientific events and in the relations with the Operations Research International community Dr Felici's strong interest in Data Mining and in the cross-fertilization between this research area and Mathematical Optimization is driven by the belief that the amazing progress made in the last decades in the field of mathematical programming can provide enormous benefits if it is applied to the construction of intelligent systems with recognition, learning, and rule induction capabilities