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10.1 GETTING STARTED 371(a)(b)(c)Figure 10.2 Weather data: (a) spreadsheet, (b) CSV format, and (c) ARFF.interface choices at the bottom. (The others were mentioned earlier: Simple CLIis the old-fashioned command-line interface.)What you see next is the main Explorer screen, shown in Figure 10.3(b). Actually,the figure shows what it will look like after you have loaded in the weatherdata. The six tabs along the top are the basic operations that the Explorersupports: right now we are on Preprocess. Click the Open file button to
372 CHAPTER 10 | THE EXPLORER(a)(b)Figure 10.3 The Weka Explorer: (a) choosing the Explorer interface and (b) reading inthe weather data.bring up a standard dialog through which you can select a file. Choose theweather.arff file. If you have it in CSV format, change from ARFF data files toCSV data files. When you specify a .csv file it is automatically converted intoARFF format.Having loaded the file, the screen will be as shown in Figure 10.3(b). Thistells you about the dataset: it has 14 instances and five attributes (center left);the attributes are called outlook, temperature, humidity, windy, and play (lowerleft). The first attribute, outlook, is selected by default (you can choose othersby clicking them) and has no missing values, three distinct values, and no uniquevalues; the actual values are sunny, overcast, and rainy, and they occur five, four,and five times, respectively (center right). A histogram at the lower right showshow often each of the two values of the class, play, occurs for each value of theoutlook attribute. The attribute outlook is used because it appears in the boxabove the histogram, but you can draw a histogram of any other attributeinstead. Here play is selected as the class attribute; it is used to color the histogram,and any filters that require a class value use it too.The outlook attribute in Figure 10.3(b) is nominal. If you select a numericattribute, you see its minimum and maximum values, mean, and standard
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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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Page 372 and 373: 7.6 USING UNLABELED DATA 339automat
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Page 406 and 407: 10.1 GETTING STARTED 373deviation.
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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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