Data Mining: Practical Machine Learning Tools and ... - LIDeCC

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ForewordJim Gray, Series EditorMicrosoft ResearchTechnology now allows us to capture and store vast quantities of data. Findingpatterns, trends, and anomalies in these datasets, and summarizing themwith simple quantitative models, is one of the grand challenges of the informationage—turning data into information and turning information intoknowledge.There has been stunning progress in data mining and machine learning. Thesynthesis of statistics, machine learning, information theory, and computing hascreated a solid science, with a firm mathematical base, and with very powerfultools. Witten and Frank present much of this progress in this book and in thecompanion implementation of the key algorithms. As such, this is a milestonein the synthesis of data mining, data analysis, information theory, and machinelearning. If you have not been following this field for the last decade, this is agreat way to catch up on this exciting progress. If you have, then Witten andFrank’s presentation and the companion open-source workbench, called Weka,will be a useful addition to your toolkit.They present the basic theory of automatically extracting models from data,and then validating those models. The book does an excellent job of explainingthe various models (decision trees, association rules, linear models, clustering,Bayes nets, neural nets) and how to apply them in practice. With this basis, theythen walk through the steps and pitfalls of various approaches. They describehow to safely scrub datasets, how to build models, and how to evaluate a model’spredictive quality. Most of the book is tutorial, but Part II broadly describes howcommercial systems work and gives a tour of the publicly available data miningworkbench that the authors provide through a website. This Weka workbenchhas a graphical user interface that leads you through data mining tasks and hasexcellent data visualization tools that help understand the models. It is a greatcompanion to the text and a useful and popular tool in its own right.v
viFOREWORDThis book presents this new discipline in a very accessible form: as a textboth to train the next generation of practitioners and researchers and to informlifelong learners like myself. Witten and Frank have a passion for simple andelegant solutions. They approach each topic with this mindset, grounding allconcepts in concrete examples, and urging the reader to consider the simpletechniques first, and then progress to the more sophisticated ones if the simpleones prove inadequate.If you are interested in databases, and have not been following the machinelearning field, this book is a great way to catch up on this exciting progress. Ifyou have data that you want to analyze and understand, this book and the associatedWeka toolkit are an excellent way to start.
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Page 10 and 11: CONTENTSix4 Algorithms: The basic m
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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 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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chapter 12The ExperimenterThe Explo
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12.1 GETTING STARTED 439Dataset,Run
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12.2 SIMPLE SETUP 441cance test of
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12.4 THE ANALYZE PANEL 443advanced
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12.5 DISTRIBUTING PROCESSING OVER S
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12.5 DISTRIBUTING PROCESSING OVER S
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450 CHAPTER 13 | THE COMMAND-LINE I
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chapter 14Embedded Machine Learning
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14.2 GOING THROUGH THE CODE 463/***
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14.2 GOING THROUGH THE CODE 465// I
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14.2 GOING THROUGH THE CODE 467}//
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14.2 GOING THROUGH THE CODE 469filt
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472 CHAPTER 15 | WRITING NEW LEARNI
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ReferencesAdriaans, P., and D. Zant
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Bouckaert, R. R. 2004. Bayesian net
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REFERENCES 489Cypher, A., editor. 1
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REFERENCES 491Fix, E., and J. L. Ho
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REFERENCES 493Gennari, J. H., P. La
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Conference on Knowledge Discovery a
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REFERENCES 497Kushmerick, N., D. S.
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Moore, A. W., and M. S. Lee. 1994.
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REFERENCES 501editor, Proceedings o
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REFERENCES 503Webb, G. I., J. Bough
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506 INDEXanomaly detection systems,
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508 INDEXcausal relations, 350CfsSu
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510 INDEXCSVLoader, 381cumulative m
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512 INDEXExperimenter, 437-447advan
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514 INDEXimplementation—real-worl
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516 INDEXlistOptions(), 482literary
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518 INDEXnumeric prediction (contin
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520 INDEXrelational data, 49relatio
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522 INDEXsupport vector, 216support
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524 INDEXWinnow, 410Winnow algorith
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