Views
3 years ago

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

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

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

  • Page 2: Data MiningPractical Machine Learni
  • Page 5 and 6: Publisher:Publishing Services Manag
  • Page 7 and 8: viFOREWORDThis book presents this n
  • Page 10 and 11: CONTENTSix4 Algorithms: The basic m
  • Page 12 and 13: CONTENTSxiGenerating good rules 202
  • Page 14 and 15: CONTENTSxiii8 Moving on: Extensions
  • Page 16: CONTENTSxv13 The command-line inter
  • Page 19 and 20: xviiiLIST OF FIGURESFigure 4.10 The
  • Page 21 and 22: xxLIST OF FIGURESFigure 10.13 Worki
  • Page 23 and 24: xxiiLIST OF TABLESTable 5.2 Confide
  • Page 25 and 26: xxivPREFACEalchemy. Instead, there
  • Page 27 and 28: xxviPREFACEwho interprets them, and
  • Page 29 and 30: xxviiiPREFACEin Section 6.3. We hav
  • Page 31 and 32: xxxPREFACEration. All who have work
  • Page 34: partIMachine Learning Toolsand Tech
  • Page 37 and 38: 4 CHAPTER 1 | WHAT’S IT ALL ABOUT
  • Page 39 and 40: 6 CHAPTER 1 | WHAT’S IT ALL ABOUT
  • Page 41 and 42: 8 CHAPTER 1 | WHAT’S IT ALL ABOUT
  • Page 43 and 44: 10 CHAPTER 1 | WHAT’S IT ALL ABOU
  • Page 45 and 46: 12 CHAPTER 1 | WHAT’S IT ALL ABOU
  • Page 47 and 48: 14 CHAPTER 1 | WHAT’S IT ALL ABOU
  • Page 49 and 50: 16 CHAPTER 1 | WHAT’S IT ALL ABOU
  • Page 51 and 52: 18 CHAPTER 1 | WHAT’S IT ALL ABOU
  • Page 53 and 54:

    20 CHAPTER 1 | WHAT’S IT ALL ABOU

  • Page 55 and 56:

    22 CHAPTER 1 | WHAT’S IT ALL ABOU

  • Page 57 and 58:

    24 CHAPTER 1 | WHAT’S IT ALL ABOU

  • Page 59 and 60:

    26 CHAPTER 1 | WHAT’S IT ALL ABOU

  • Page 61 and 62:

    28 CHAPTER 1 | WHAT’S IT ALL ABOU

  • Page 63 and 64:

    30 CHAPTER 1 | WHAT’S IT ALL ABOU

  • Page 65 and 66:

    32 CHAPTER 1 | WHAT’S IT ALL ABOU

  • Page 67 and 68:

    34 CHAPTER 1 | WHAT’S IT ALL ABOU

  • Page 69 and 70:

    36 CHAPTER 1 | WHAT’S IT ALL ABOU

  • Page 71 and 72:

    38 CHAPTER 1 | WHAT’S IT ALL ABOU

  • Page 74 and 75:

    chapter 2Input:Concepts, Instances,

  • Page 76 and 77:

    2.1 WHAT’S A CONCEPT? 43increase

  • Page 78 and 79:

    2.2 WHAT’S IN AN EXAMPLE? 45data

  • Page 80 and 81:

    2.2 WHAT’S IN AN EXAMPLE? 47Table

  • Page 82 and 83:

    2.3 WHAT’S IN AN ATTRIBUTE? 49Tab

  • Page 84 and 85:

    2.3 WHAT’S IN AN ATTRIBUTE? 51Not

  • Page 86 and 87:

    2.4 PREPARING THE INPUT 53cleaned u

  • Page 88 and 89:

    missing values in this dataset). Th

  • Page 90 and 91:

    dividing by the range between the m

  • Page 92 and 93:

    2.4 PREPARING THE INPUT 59a record

  • Page 94 and 95:

    chapter 3Output:Knowledge Represent

  • Page 96 and 97:

    3.2 DECISION TREES 63Alternatively,

  • Page 98 and 99:

    3.3 CLASSIFICATION RULES 65nated by

  • Page 100 and 101:

    3.3 CLASSIFICATION RULES 671abx = 1

  • Page 102 and 103:

    3.4 ASSOCIATION RULES 69yes, then i

  • Page 104 and 105:

    3.5 RULES WITH EXCEPTIONS 71If peta

  • Page 106 and 107:

    3.6 RULES INVOLVING RELATIONS 73ica

  • Page 108 and 109:

    Standard relations include equality

  • Page 110 and 111:

    PRP =-56.1+0.049 MYCT+0.015 MMIN+0.

  • Page 112 and 113:

    3.8 INSTANCE-BASED REPRESENTATION 7

  • Page 114 and 115:

    3.9 Clusters3.9 CLUSTERS 81When clu

  • Page 116 and 117:

    chapter 4Algorithms:The Basic Metho

  • Page 118 and 119:

    4.1 INFERRING RUDIMENTARY RULES 85F

  • Page 120 and 121:

    described overfitting-avoidance bia

  • Page 122 and 123:

    4.2 STATISTICAL MODELING 89Table 4.

  • Page 124 and 125:

    just as we calculated previously. A

  • Page 126 and 127:

    4.2 STATISTICAL MODELING 93Table 4.

  • Page 128 and 129:

    of a document. Instead, a document

  • Page 130 and 131:

    4.3 DIVIDE-AND-CONQUER: CONSTRUCTIN

  • Page 132 and 133:

    4.3 DIVIDE-AND-CONQUER: CONSTRUCTIN

  • Page 134 and 135:

    4.3 DIVIDE-AND-CONQUER: CONSTRUCTIN

  • Page 136 and 137:

    4.3 DIVIDE-AND-CONQUER: CONSTRUCTIN

  • Page 138 and 139:

    4.4 COVERING ALGORITHMS: CONSTRUCTI

  • Page 140 and 141:

    4.4 COVERING ALGORITHMS: CONSTRUCTI

  • Page 142 and 143:

    4.4 COVERING ALGORITHMS: CONSTRUCTI

  • Page 144 and 145:

    4.4 COVERING ALGORITHMS: CONSTRUCTI

  • Page 146 and 147:

    4.5 MINING ASSOCIATION RULES 113acc

  • Page 148 and 149:

    4.5 MINING ASSOCIATION RULES 115Tab

  • Page 150 and 151:

    4.5 MINING ASSOCIATION RULES 117whi

  • Page 152 and 153:

    4.6 LINEAR MODELS 119through the da

  • Page 154 and 155:

    4.6 LINEAR MODELS 121However, linea

  • Page 156 and 157:

    4.6 LINEAR MODELS 123n( i)i i 1-x

  • Page 158 and 159:

    4.6 LINEAR MODELS 125Set all weight

  • Page 160 and 161:

    4.6 LINEAR MODELS 127While some ins

  • Page 162 and 163:

    4.7 INSTANCE-BASED LEARNING 129When

  • Page 164 and 165:

    4.7 INSTANCE-BASED LEARNING 131Figu

  • Page 166 and 167:

    4.7 INSTANCE-BASED LEARNING 133Figu

  • Page 168 and 169:

    4.7 INSTANCE-BASED LEARNING 135Figu

  • Page 170 and 171:

    4.8 CLUSTERING 137As we saw in Sect

  • Page 172 and 173:

    4.9 FURTHER READING 139can be updat

  • Page 174 and 175:

    4.9 FURTHER READING 141Bayes was an

  • Page 176 and 177:

    chapter 5Credibility:Evaluating Wha

  • Page 178 and 179:

    5.1 TRAINING AND TESTING 145of each

  • Page 180 and 181:

    ather than error rate, so this corr

  • Page 182 and 183:

    5.3 CROSS-VALIDATION 149mediate con

  • Page 184 and 185:

    5.4 OTHER ESTIMATES 151A single 10-

  • Page 186 and 187:

    90% used in 10-fold cross-validatio

  • Page 188 and 189:

    5.5 COMPARING DATA MINING METHODS 1

  • Page 190 and 191:

    5.6 PREDICTING PROBABILITIES 157In

  • Page 192 and 193:

    5.6 PREDICTING PROBABILITIES 159whe

  • Page 194 and 195:

    mental job expected of a loss funct

  • Page 196 and 197:

    y the total number of positives, wh

  • Page 198 and 199:

    5.7 COUNTING THE COST 165different

  • Page 200 and 201:

    5.7 COUNTING THE COST 167Table 5.6D

  • Page 202 and 203:

    5.7 COUNTING THE COST 169100%80%tru

  • Page 204 and 205:

    5.7 COUNTING THE COST 171should cho

  • Page 206 and 207:

    Different terms are used in differe

  • Page 208 and 209:

    5.7 COUNTING THE COST 1750.5Anormal

  • Page 210 and 211:

    5.8 EVALUATING NUMERIC PREDICTION 1

  • Page 212 and 213:

    5.9 THE MINIMUM DESCRIPTION LENGTH

  • Page 214 and 215:

    5.9 THE MINIMUM DESCRIPTION LENGTH

  • Page 216 and 217:

    5.10 APPLYING THE MDL PRINCIPLE TO

  • Page 218:

    5.11 FURTHER READING 185tion theory

  • Page 221 and 222:

    188 CHAPTER 6 | IMPLEMENTATIONS: RE

  • Page 223 and 224:

    190 CHAPTER 6 | IMPLEMENTATIONS: RE

  • Page 225 and 226:

    192 CHAPTER 6 | IMPLEMENTATIONS: RE

  • Page 227 and 228:

    194 CHAPTER 6 | IMPLEMENTATIONS: RE

  • Page 229 and 230:

    196 CHAPTER 6 | IMPLEMENTATIONS: RE

  • Page 231 and 232:

    198 CHAPTER 6 | IMPLEMENTATIONS: RE

  • Page 233 and 234:

    200 CHAPTER 6 | IMPLEMENTATIONS: RE

  • Page 235 and 236:

    202 CHAPTER 6 | IMPLEMENTATIONS: RE

  • Page 237 and 238:

    204 CHAPTER 6 | IMPLEMENTATIONS: RE

  • Page 239:

    206 CHAPTER 6 | IMPLEMENTATIONS: RE

  • Page 243 and 244:

    210 CHAPTER 6 | IMPLEMENTATIONS: RE

  • Page 245 and 246:

    212 CHAPTER 6 | IMPLEMENTATIONS: RE

  • Page 247 and 248:

    214 CHAPTER 6 | IMPLEMENTATIONS: RE

  • Page 249 and 250:

    216 CHAPTER 6 | IMPLEMENTATIONS: RE

  • Page 251 and 252:

    218 CHAPTER 6 | IMPLEMENTATIONS: RE

  • Page 253 and 254:

    220 CHAPTER 6 | IMPLEMENTATIONS: RE

  • Page 255 and 256:

    222 CHAPTER 6 | IMPLEMENTATIONS: RE

  • Page 257 and 258:

    224 CHAPTER 6 | IMPLEMENTATIONS: RE

  • Page 259 and 260:

    226 CHAPTER 6 | IMPLEMENTATIONS: RE

  • Page 261 and 262:

    228 CHAPTER 6 | IMPLEMENTATIONS: RE

  • Page 263 and 264:

    230 CHAPTER 6 | IMPLEMENTATIONS: RE

  • Page 265 and 266:

    232 CHAPTER 6 | IMPLEMENTATIONS: RE

  • Page 267 and 268:

    234 CHAPTER 6 | IMPLEMENTATIONS: RE

  • Page 269 and 270:

    236 CHAPTER 6 | IMPLEMENTATIONS: RE

  • Page 271 and 272:

    238 CHAPTER 6 | IMPLEMENTATIONS: RE

  • Page 273 and 274:

    240 CHAPTER 6 | IMPLEMENTATIONS: RE

  • Page 275 and 276:

    242 CHAPTER 6 | IMPLEMENTATIONS: RE

  • Page 277 and 278:

    244 CHAPTER 6 | IMPLEMENTATIONS: RE

  • Page 279 and 280:

    246 CHAPTER 6 | IMPLEMENTATIONS: RE

  • Page 281 and 282:

    248 CHAPTER 6 | IMPLEMENTATIONS: RE

  • Page 283 and 284:

    250 CHAPTER 6 | IMPLEMENTATIONS: RE

  • Page 285 and 286:

    252 CHAPTER 6 | IMPLEMENTATIONS: RE

  • Page 287 and 288:

    254 CHAPTER 6 | IMPLEMENTATIONS: RE

  • Page 289 and 290:

    256 CHAPTER 6 | IMPLEMENTATIONS: RE

  • Page 291 and 292:

    258 CHAPTER 6 | IMPLEMENTATIONS: RE

  • Page 293 and 294:

    260 CHAPTER 6 | IMPLEMENTATIONS: RE

  • Page 295 and 296:

    262 CHAPTER 6 | IMPLEMENTATIONS: RE

  • Page 297 and 298:

    264 CHAPTER 6 | IMPLEMENTATIONS: RE

  • Page 299 and 300:

    266 CHAPTER 6 | IMPLEMENTATIONS: RE

  • Page 301 and 302:

    268 CHAPTER 6 | IMPLEMENTATIONS: RE

  • Page 303 and 304:

    270 CHAPTER 6 | IMPLEMENTATIONS: RE

  • Page 305 and 306:

    272 CHAPTER 6 | IMPLEMENTATIONS: RE

  • Page 307 and 308:

    274 CHAPTER 6 | IMPLEMENTATIONS: RE

  • Page 309 and 310:

    276 CHAPTER 6 | IMPLEMENTATIONS: RE

  • Page 311 and 312:

    278 CHAPTER 6 | IMPLEMENTATIONS: RE

  • Page 313 and 314:

    280 CHAPTER 6 | IMPLEMENTATIONS: RE

  • Page 315 and 316:

    282 CHAPTER 6 | IMPLEMENTATIONS: RE

  • Page 318 and 319:

    chapter 7Transformations:Engineerin

  • Page 320 and 321:

    7.1 ATTRIBUTE SELECTION 287attribut

  • Page 322 and 323:

    7.1 ATTRIBUTE SELECTION 289and less

  • Page 324 and 325:

    tion—and it is much easier to und

  • Page 326 and 327:

    7.1 ATTRIBUTE SELECTION 293outlook

  • Page 328 and 329:

    7.1 ATTRIBUTE SELECTION 295the t-te

  • Page 330 and 331:

    7.2 DISCRETIZING NUMERIC ATTRIBUTES

  • Page 332 and 333:

    7.2 DISCRETIZING NUMERIC ATTRIBUTES

  • Page 334 and 335:

    7.2 DISCRETIZING NUMERIC ATTRIBUTES

  • Page 336 and 337:

    7.2 DISCRETIZING NUMERIC ATTRIBUTES

  • Page 338 and 339:

    7.3 SOME USEFUL TRANSFORMATIONS 305

  • Page 340 and 341:

    7.3 SOME USEFUL TRANSFORMATIONS 307

  • Page 342 and 343:

    7.3 SOME USEFUL TRANSFORMATIONS 309

  • Page 344 and 345:

    7.3 SOME USEFUL TRANSFORMATIONS 311

  • Page 346 and 347:

    7.4 AUTOMATIC DATA CLEANSING 313Int

  • Page 348 and 349:

    7.5 COMBINING MULTIPLE MODELS 315da

  • Page 350 and 351:

    7.5 COMBINING MULTIPLE MODELS 317pa

  • Page 352 and 353:

    7.5 COMBINING MULTIPLE MODELS 319mo

  • Page 354 and 355:

    7.5 COMBINING MULTIPLE MODELS 321Ra

  • Page 356 and 357:

    7.5 COMBINING MULTIPLE MODELS 323Ho

  • Page 358 and 359:

    7.5 COMBINING MULTIPLE MODELS 325Th

  • Page 360 and 361:

    7.5 COMBINING MULTIPLE MODELS 327of

  • Page 362 and 363:

    7.5 COMBINING MULTIPLE MODELS 329ou

  • Page 364 and 365:

    7.5 COMBINING MULTIPLE MODELS 331ev

  • Page 366 and 367:

    7.5 COMBINING MULTIPLE MODELS 333be

  • Page 368 and 369:

    7.5 COMBINING MULTIPLE MODELS 335Ta

  • Page 370 and 371:

    7.6 Using unlabeled data7.6 USING U

  • Page 372 and 373:

    7.6 USING UNLABELED DATA 339automat

  • Page 374 and 375:

    7.7 FURTHER READING 341deal with we

  • Page 376:

    7.7 FURTHER READING 343Domingos (19

  • Page 379 and 380:

    346 CHAPTER 8 | MOVING ON: EXTENSIO

  • Page 381 and 382:

    348 CHAPTER 8 | MOVING ON: EXTENSIO

  • Page 383 and 384:

    350 CHAPTER 8 | MOVING ON: EXTENSIO

  • Page 385 and 386:

    352 CHAPTER 8 | MOVING ON: EXTENSIO

  • Page 387 and 388:

    354 CHAPTER 8 | MOVING ON: EXTENSIO

  • Page 389 and 390:

    356 CHAPTER 8 | MOVING ON: EXTENSIO

  • Page 391 and 392:

    358 CHAPTER 8 | MOVING ON: EXTENSIO

  • Page 393 and 394:

    360 CHAPTER 8 | MOVING ON: EXTENSIO

  • Page 395 and 396:

    362 CHAPTER 8 | MOVING ON: EXTENSIO

  • Page 398 and 399:

    chapter 9Introduction to WekaExperi

  • Page 400 and 401:

    9.2 How do you use it?9.2 HOW DO YO

  • Page 402 and 403:

    chapter 10The ExplorerWeka’s main

  • Page 404 and 405:

    10.1 GETTING STARTED 371(a)(b)(c)Fi

  • Page 406 and 407:

    10.1 GETTING STARTED 373deviation.

  • Page 408 and 409:

    10.1 GETTING STARTED 375=== Run inf

  • Page 410 and 411:

    10.1 GETTING STARTED 377Doing it ag

  • Page 412 and 413:

    10.1 GETTING STARTED 379(a)(b)Figur

  • Page 414 and 415:

    10.2 EXPLORING THE EXPLORER 381(a)(

  • Page 416 and 417:

    10.2 EXPLORING THE EXPLORER 383(b)(

  • Page 418 and 419:

    10.2 EXPLORING THE EXPLORER 385(a)(

  • Page 420 and 421:

    10.2 EXPLORING THE EXPLORER 387+ 0.

  • Page 422 and 423:

    10.2 EXPLORING THE EXPLORER 389posi

  • Page 424 and 425:

    10.2 EXPLORING THE EXPLORER 391Figu

  • Page 426 and 427:

    10.3 FILTERING ALGORITHMS 393method

  • Page 428 and 429:

    10.3 FILTERING ALGORITHMS 395(b)Fig

  • Page 430 and 431:

    10.3 FILTERING ALGORITHMS 397or fir

  • Page 432 and 433:

    10.3 FILTERING ALGORITHMS 399attrib

  • Page 434 and 435:

    10.3 FILTERING ALGORITHMS 401it int

  • Page 436 and 437:

    10.4 LEARNING ALGORITHMS 403There i

  • Page 438 and 439:

    10.4 LEARNING ALGORITHMS 405Table 1

  • Page 440 and 441:

    10.4 LEARNING ALGORITHMS 407Figure

  • Page 442 and 443:

    10.4 LEARNING ALGORITHMS 409value (

  • Page 444 and 445:

    10.4 LEARNING ALGORITHMS 411Neural

  • Page 446 and 447:

    10.4 LEARNING ALGORITHMS 413there a

  • Page 448 and 449:

    10.5 METALEARNING ALGORITHMS 415Tab

  • Page 450 and 451:

    10.5 METALEARNING ALGORITHMS 417Com

  • Page 452 and 453:

    10.7 ASSOCIATION-RULE LEARNERS 419N

  • Page 454 and 455:

    10.8 ATTRIBUTE SELECTION 421Table 1

  • Page 456 and 457:

    10.8 ATTRIBUTE SELECTION 423tribute

  • Page 458:

    10.8 ATTRIBUTE SELECTION 425with on

  • Page 461 and 462:

    428 CHAPTER 11 | THE KNOWLEDGE FLOW

  • Page 463 and 464:

    430 CHAPTER 11 | THE KNOWLEDGE FLOW

  • Page 465 and 466:

    432 CHAPTER 11 | THE KNOWLEDGE FLOW

  • 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

  • Page 480:

    12.5 DISTRIBUTING PROCESSING OVER S

  • Page 483 and 484:

    450 CHAPTER 13 | THE COMMAND-LINE I

  • Page 485 and 486:

    452 CHAPTER 13 | THE COMMAND-LINE I

  • Page 487 and 488:

    454 CHAPTER 13 | THE COMMAND-LINE I

  • Page 489 and 490:

    456 CHAPTER 13 | THE COMMAND-LINE I

  • Page 491 and 492:

    458 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 507 and 508:

    474 CHAPTER 15 | WRITING NEW LEARNI

  • Page 509 and 510:

    476 CHAPTER 15 | WRITING NEW LEARNI

  • Page 511 and 512:

    478 CHAPTER 15 | WRITING NEW LEARNI

  • Page 513 and 514:

    480 CHAPTER 15 | WRITING NEW LEARNI

  • Page 515 and 516:

    482 CHAPTER 15 | WRITING NEW LEARNI

  • Page 518 and 519:

    ReferencesAdriaans, P., and D. Zant

  • 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

Machine Learning and Data Mining - Electrical & Computer ...
Privacy Preserving Data Mining - ADReM
Data Mining with the Tools You Already Have - BI User Group
Machine Learning - Tom Mitchell.pdf
Data Mining with the Tools You Already Have - Digital Concourse
Introduction to Machine Learning - ArrestedComputing
Practical Data Mining: Lessons Learned From the Barnett Shale of ...
Machine learning
INFORMATION & DATA MINING TOOL FOR NORTH ... - ifremer
Data Visualization in Data Mining - Chemical Engineering and ...
[1] Data Mining - Concepts and Techniques (3rd Ed)
Building Machine Learning Systems with Python - Richert, Coelho
data mining - SNAP - Stanford University
Embedding Data Mining Technology in E-Commerce Applications
Data Mining Methods and Models - Trisakti Blogger Community
Learning-practice: The Ghosts in the Education Machine
Mining archival data using VO tools; - ESO
Machine - Learning - Tom Mitchell
Computer security: A machine learning approach
US Data Mining Initiative: What Have We Learned - Safequarry.com
Tools for sequence data mining: HMMER (part 2)
Using Genetic Algorithms for Data Mining Optimization ... - Lon Capa