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ReferencesAdriaans, P., and D. Zantige. 1996. Data mining. Harlow, England: Addison-Wesley.Agrawal, R., and R. Srikant. 1994. Fast algorithms for mining association rules inlarge databases. In J. Bocca, M. Jarke, and C. Zaniolo, editors, Proceedings ofthe International Conference on Very Large Databases, Santiago, Chile. SanFrancisco: Morgan Kaufmann, pp. 478–499.Agrawal, R., T. Imielinski, and A. Swami. 1993a. Database mining: A performanceperspective. IEEE Transactions on Knowledge and Data Engineering 5(6):914–925.———. 1993b. Mining association rules between sets of items in large databases.In P. Buneman and S. Jajodia, editors, Proceedings of the ACM SIGMOD InternationalConference on Management of Data, Washington, DC. New York:ACM, pp. 207–216.Aha, D. 1992. Tolerating noisy, irrelevant, and novel attributes in instancebasedlearning algorithms. International Journal of Man-Machine Studies36(2):267–287.Almuallin, H., and T. G. Dietterich. 1991. Learning with many irrelevant features.In Proceedings of the Ninth National Conference on Artificial Intelligence,Anaheim, CA. Menlo Park, CA: AAAI Press, pp. 547–552.———. 1992. Efficient algorithms for identifying relevant features. In Proceedingsof the Ninth Canadian Conference on Artificial Intelligence, Vancouver, BC. SanFrancisco: Morgan Kaufmann, pp. 38–45.Appelt, D. E. 1999. Introduction to information extraction technology. Tutorial, IntJoint Conf on Artificial Intelligence IJCAI’99. Morgan Kaufmann, San Mateo.Tutorial notes available at www.ai.sri.com/~appelt/ie-tutorial.485
486 REFERENCESAsmis, E. 1984. Epicurus’ scientific method. Ithaca, NY: Cornell University Press.Atkeson, C. G., S. A. Schaal, and A. W. Moore. 1997. Locally weighted learning. AIReview 11:11–71.Bay, S. D. 1999. Nearest-neighbor classification from multiple feature subsets.Intelligent Data Analysis 3(3):191–209.Bay, S. D., and M. Schwabacher. 2003. Near linear time detection of distance-basedoutliers and applications to security. In Proceedings of the Workshop on DataMining for Counter Terrorism and Security, San Francisco. Society forIndustrial and Applied Mathematics, Philadelphia, PA.Bayes, T. 1763. An essay towards solving a problem in the doctrine of chances.Philosophical Transactions of the Royal Society of London 53:370–418.Beck, J. R., and E. K. Schultz. 1986. The use of ROC curves in test performance evaluation.Archives of Pathology and Laboratory Medicine 110:13–20.Bergadano, F., and D. Gunetti. 1996. Inductive logic programming: From machinelearning to software engineering. Cambridge, MA: MIT Press.Berners-Lee, T., J. Hendler, and O. Lassila. 2001. The semantic web. ScientificAmerican 284(5):34–43.Berry, M. J. A., and G. Linoff. 1997. Data mining techniques for marketing, sales, andcustomer support. New York: John Wiley.Bigus, J. P. 1996. Data mining with neural networks. New York: McGraw Hill.Bishop, C. M. 1995. Neural networks for pattern recognition. New York: OxfordUniversity Press.Blake, C., E. Keogh, and C. J. Merz. 1998. UCI Repository of machine learningdatabases [http://www.ics.uci.edu/~mlearn/MLRepository.html]. Departmentof Information and Computer Science, University of California, Irvine,CA.BLI (Bureau of Labour Information). 1988. Collective Bargaining Review(November). Ottawa, Ontario, Canada: Labour Canada, Bureau of LabourInformation.Blum, A., and T. Mitchell. 1998. Combining labeled and unlabeled data with cotraining.In Proceedings of the Eleventh Annual Conference on ComputationalLearning Theory, Madison, WI. San Francisco: Morgan Kaufmann, pp. 92–100.Bouckaert, R. R. 1995. Bayesian belief networks: From construction to inference. PhDDissertation, Computer Science Department, University of Utrecht, TheNetherlands.
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Data MiningPractical Machine Learni
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Publisher:Publishing Services Manag
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viFOREWORDThis book presents this n
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CONTENTSix4 Algorithms: The basic m
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CONTENTSxiGenerating good rules 202
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CONTENTSxiii8 Moving on: Extensions
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CONTENTSxv13 The command-line inter
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xviiiLIST OF FIGURESFigure 4.10 The
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xxLIST OF FIGURESFigure 10.13 Worki
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xxiiLIST OF TABLESTable 5.2 Confide
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xxivPREFACEalchemy. Instead, there
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xxviPREFACEwho interprets them, and
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xxviiiPREFACEin Section 6.3. We hav
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xxxPREFACEration. All who have work
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partIMachine Learning Toolsand Tech
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4 CHAPTER 1 | WHAT’S IT ALL ABOUT
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10 CHAPTER 1 | WHAT’S IT ALL ABOU
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chapter 2Input:Concepts, Instances,
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2.1 WHAT’S A CONCEPT? 43increase
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2.2 WHAT’S IN AN EXAMPLE? 45data
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2.2 WHAT’S IN AN EXAMPLE? 47Table
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2.3 WHAT’S IN AN ATTRIBUTE? 49Tab
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2.3 WHAT’S IN AN ATTRIBUTE? 51Not
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2.4 PREPARING THE INPUT 53cleaned u
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missing values in this dataset). Th
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dividing by the range between the m
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2.4 PREPARING THE INPUT 59a record
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chapter 3Output:Knowledge Represent
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3.2 DECISION TREES 63Alternatively,
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3.3 CLASSIFICATION RULES 65nated by
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3.3 CLASSIFICATION RULES 671abx = 1
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3.4 ASSOCIATION RULES 69yes, then i
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3.5 RULES WITH EXCEPTIONS 71If peta
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3.6 RULES INVOLVING RELATIONS 73ica
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Standard relations include equality
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PRP =-56.1+0.049 MYCT+0.015 MMIN+0.
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3.8 INSTANCE-BASED REPRESENTATION 7
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3.9 Clusters3.9 CLUSTERS 81When clu
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chapter 4Algorithms:The Basic Metho
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4.1 INFERRING RUDIMENTARY RULES 85F
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described overfitting-avoidance bia
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4.2 STATISTICAL MODELING 89Table 4.
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just as we calculated previously. A
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4.2 STATISTICAL MODELING 93Table 4.
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of a document. Instead, a document
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4.3 DIVIDE-AND-CONQUER: CONSTRUCTIN
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4.4 COVERING ALGORITHMS: CONSTRUCTI
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4.5 MINING ASSOCIATION RULES 113acc
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4.5 MINING ASSOCIATION RULES 115Tab
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4.5 MINING ASSOCIATION RULES 117whi
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4.6 LINEAR MODELS 119through the da
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4.6 LINEAR MODELS 121However, linea
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4.6 LINEAR MODELS 123n( i)i iÂ 1-x
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4.6 LINEAR MODELS 125Set all weight
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4.6 LINEAR MODELS 127While some ins
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4.7 INSTANCE-BASED LEARNING 129When
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4.7 INSTANCE-BASED LEARNING 131Figu
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4.8 CLUSTERING 137As we saw in Sect
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4.9 FURTHER READING 139can be updat
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4.9 FURTHER READING 141Bayes was an
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chapter 5Credibility:Evaluating Wha
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5.1 TRAINING AND TESTING 145of each
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ather than error rate, so this corr
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5.3 CROSS-VALIDATION 149mediate con
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5.4 OTHER ESTIMATES 151A single 10-
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90% used in 10-fold cross-validatio
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5.5 COMPARING DATA MINING METHODS 1
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5.6 PREDICTING PROBABILITIES 157In
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5.6 PREDICTING PROBABILITIES 159whe
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mental job expected of a loss funct
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y the total number of positives, wh
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5.7 COUNTING THE COST 165different
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5.7 COUNTING THE COST 167Table 5.6D
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5.7 COUNTING THE COST 169100%80%tru
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5.7 COUNTING THE COST 171should cho
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Different terms are used in differe
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5.7 COUNTING THE COST 1750.5Anormal
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5.8 EVALUATING NUMERIC PREDICTION 1
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5.9 THE MINIMUM DESCRIPTION LENGTH
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5.9 THE MINIMUM DESCRIPTION LENGTH
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5.10 APPLYING THE MDL PRINCIPLE TO
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5.11 FURTHER READING 185tion theory
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188 CHAPTER 6 | IMPLEMENTATIONS: RE
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chapter 7Transformations:Engineerin
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7.1 ATTRIBUTE SELECTION 287attribut
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7.1 ATTRIBUTE SELECTION 289and less
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tion—and it is much easier to und
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7.1 ATTRIBUTE SELECTION 293outlook
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7.1 ATTRIBUTE SELECTION 295the t-te
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7.2 DISCRETIZING NUMERIC ATTRIBUTES
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7.3 SOME USEFUL TRANSFORMATIONS 305
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7.4 AUTOMATIC DATA CLEANSING 313Int
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7.5 COMBINING MULTIPLE MODELS 315da
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7.5 COMBINING MULTIPLE MODELS 317pa
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7.5 COMBINING MULTIPLE MODELS 319mo
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7.5 COMBINING MULTIPLE MODELS 321Ra
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7.5 COMBINING MULTIPLE MODELS 323Ho
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7.5 COMBINING MULTIPLE MODELS 325Th
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7.5 COMBINING MULTIPLE MODELS 327of
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7.5 COMBINING MULTIPLE MODELS 333be
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7.5 COMBINING MULTIPLE MODELS 335Ta
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7.6 Using unlabeled data7.6 USING U
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7.6 USING UNLABELED DATA 339automat
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7.7 FURTHER READING 341deal with we
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7.7 FURTHER READING 343Domingos (19
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346 CHAPTER 8 | MOVING ON: EXTENSIO
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chapter 9Introduction to WekaExperi
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9.2 How do you use it?9.2 HOW DO YO
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chapter 10The ExplorerWeka’s main
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10.1 GETTING STARTED 371(a)(b)(c)Fi
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10.1 GETTING STARTED 373deviation.
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10.1 GETTING STARTED 375=== Run inf
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10.1 GETTING STARTED 377Doing it ag
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10.1 GETTING STARTED 379(a)(b)Figur
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10.2 EXPLORING THE EXPLORER 381(a)(
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10.2 EXPLORING THE EXPLORER 383(b)(
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10.2 EXPLORING THE EXPLORER 385(a)(
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10.2 EXPLORING THE EXPLORER 387+ 0.
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10.2 EXPLORING THE EXPLORER 389posi
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10.2 EXPLORING THE EXPLORER 391Figu
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10.3 FILTERING ALGORITHMS 393method
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10.3 FILTERING ALGORITHMS 395(b)Fig
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10.3 FILTERING ALGORITHMS 397or fir
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10.3 FILTERING ALGORITHMS 399attrib
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10.3 FILTERING ALGORITHMS 401it int
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10.4 LEARNING ALGORITHMS 403There i
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10.4 LEARNING ALGORITHMS 405Table 1
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10.4 LEARNING ALGORITHMS 407Figure
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10.4 LEARNING ALGORITHMS 409value (
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10.4 LEARNING ALGORITHMS 411Neural
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10.4 LEARNING ALGORITHMS 413there a
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10.5 METALEARNING ALGORITHMS 415Tab
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10.5 METALEARNING ALGORITHMS 417Com
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10.7 ASSOCIATION-RULE LEARNERS 419N
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10.8 ATTRIBUTE SELECTION 421Table 1
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10.8 ATTRIBUTE SELECTION 423tribute
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10.8 ATTRIBUTE SELECTION 425with on
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428 CHAPTER 11 | THE KNOWLEDGE FLOW
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Page 467 and 468: 434 CHAPTER 11 | THE KNOWLEDGE FLOW
Page 470 and 471: chapter 12The ExperimenterThe Explo
Page 472 and 473: 12.1 GETTING STARTED 439Dataset,Run
Page 474 and 475: 12.2 SIMPLE SETUP 441cance test of
Page 476 and 477: 12.4 THE ANALYZE PANEL 443advanced
Page 478 and 479: 12.5 DISTRIBUTING PROCESSING OVER S
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Page 483 and 484: 450 CHAPTER 13 | THE COMMAND-LINE I
Page 494 and 495: chapter 14Embedded Machine Learning
Page 496 and 497: 14.2 GOING THROUGH THE CODE 463/***
Page 498 and 499: 14.2 GOING THROUGH THE CODE 465// I
Page 500 and 501: 14.2 GOING THROUGH THE CODE 467}//
Page 502: 14.2 GOING THROUGH THE CODE 469filt
Page 505 and 506: 472 CHAPTER 15 | WRITING NEW LEARNI
Page 520 and 521: Bouckaert, R. R. 2004. Bayesian net
Page 522 and 523: REFERENCES 489Cypher, A., editor. 1
Page 524 and 525: REFERENCES 491Fix, E., and J. L. Ho
Page 526 and 527: REFERENCES 493Gennari, J. H., P. La
Page 528 and 529: Conference on Knowledge Discovery a
Page 530 and 531: REFERENCES 497Kushmerick, N., D. S.
Page 532 and 533: Moore, A. W., and M. S. Lee. 1994.
Page 534 and 535: REFERENCES 501editor, Proceedings o
Page 536: REFERENCES 503Webb, G. I., J. Bough
Page 539 and 540: 506 INDEXanomaly detection systems,
Page 541 and 542: 508 INDEXcausal relations, 350CfsSu
Page 543 and 544: 510 INDEXCSVLoader, 381cumulative m
Page 545 and 546: 512 INDEXExperimenter, 437-447advan
Page 547 and 548: 514 INDEXimplementation—real-worl
Page 549 and 550: 516 INDEXlistOptions(), 482literary
Page 551 and 552: 518 INDEXnumeric prediction (contin
Page 553 and 554: 520 INDEXrelational data, 49relatio
Page 555 and 556: 522 INDEXsupport vector, 216support
Page 557 and 558: 524 INDEXWinnow, 410Winnow algorith
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