TESI DOCTORAL - La Salle

More documents

Recommendations

Info

2.2. Related work on consensus functions sensus function allows assigning distinct weights to each clustering in the cluster ensemble, which can be useful for the user to express his/her confidence on the quality of some individual clusterings. The ITK consensus function of (Punera and Ghosh, 2007) also allows to assign distinct weights to each clustering in the cluster ensemble, which can be useful for the user to express his/her confidence on the quality of some individual clusterings. 44
Chapter 3 Hierarchical consensus architectures As outlined in section 1.5, our proposal for building robust multimedia clustering systems lies on the creation of consensus clustering solutions upon cluster ensembles. These ensembles are made up of a large number of individual clusterings resulting from the execution of multiple clustering algorithms on several unimodal and multimodal representations of the objects contained in the data set. Indeed, the massive crossing between clustering algorithms, object representations and data modalities is a simple and parallelizable manner of generating highly diverse heterogeneous cluster ensembles, entrusting the obtention of a meaningful combined clustering solution to the consensus clustering task. Given the unsupervised nature of the clustering problem, we think this is a pretty sensible way of proceeding so as to obtain clustering solutions robust to the influence of the clustering indeterminacies, as sticking to the use of a handful of clustering algorithms or object representations can lead to an involuntary and undesirable limitation as regards the quality and diversity of the cluster ensemble components. However, at the same time that this strategy allows the creation of rich cluster ensembles, it also introduces several drawbacks that affect the consensus clustering task: – the large number of individual clustering solutions contained in the cluster ensemble, resulting from the aforementioned combination of clustering algorithms, object representations and data modalities, often leads to a notable increase in the computational cost of the execution of the consensus function, which can even become prohibitive. – this same fact incides in the diversity and quality of the cluster ensemble components, and, while moderate diversity has been found to be beneficial as far as consensus clustering is concerned (Hadjitodorov, Kuncheva, and Todorova, 2006; Fern and Lin, 2008), the existence of poor quality clustering solutions in the cluster ensemble may cause a detrimental effect on the quality of the consensus clustering solution. Allowing for these considerations, in this thesis we introduce the concept of self-refining hierarchical consensus architectures (SHCA), defined as a generic means for fighting against: 45
Page 1:
C.I.F. G: 59069740 Universitat Ramo
Page 5:
Resum En segmentar de forma no supe
Page 9:
Abstract When facing the task of pa
Page 12 and 13:
Contents 2.2.6 Consensus functions
Page 14 and 15:
Contents A.5 Consensus functions .
Page 16 and 17:
Contents D.2.5 WDBC data set . . .
Page 18 and 19:
List of Tables 3.13 Relative percen
Page 20 and 21:
List of Tables 5.15 Relative φ (NM
Page 23 and 24:
List of Figures 1.1 Evolution of th
Page 25 and 26:
List of Figures 4.2 Decreasingly or
Page 27 and 28:
List of Figures C.18 Estimated and
Page 29 and 30: List of Figures C.49 φ (NMI) of th
Page 31 and 32: List of Figures D.22 φ (NMI) boxpl
Page 33: List of Algorithms 6.1 Symbolic des
Page 36 and 37: List of symbols OΛ: object co-asso
Page 38 and 39: Chapter 1. Framework of the thesis
Page 40 and 41: 1.1. Knowledge discovery and data m
Page 42 and 43: 1.1. Knowledge discovery and data m
Page 44 and 45: 1.2. Clustering in knowledge discov
Page 54 and 55: 1.3. Multimodal clustering in clust
Page 56 and 57: 1.4. Clustering indeterminacies exe
Page 58 and 59: 1.4. Clustering indeterminacies The
Page 60 and 61: 1.4. Clustering indeterminacies Dat
Page 62 and 63: 1.5. Motivation and contributions o
Page 64 and 65: Chapter 2. Cluster ensembles and co
Page 66 and 67: fuzzy consensus clustering solution
Page 68 and 69: 2.1. Related work on cluster ensemb
Page 70 and 71: 2.2. Related work on consensus func
Page 82 and 83: 3.1. Motivation - the computational
Page 84 and 85: 3.1. Motivation An additional and v
Page 86 and 87: 3.2. Random hierarchical consensus
Page 106 and 107: 3.3. Deterministic hierarchical con
Page 124 and 125: 3.4. Flat vs. hierarchical consensu
Page 130 and 131:
3.4. Flat vs. hierarchical consensu
Page 132 and 133:
Page 134 and 135:
Page 136 and 137:
Page 138 and 139:
Page 140 and 141:
3.5. Discussion Consensus Consensus
Page 142 and 143:
3.5. Discussion separate clustering
Page 145 and 146:
Chapter 4 Self-refining consensus a
Page 147 and 148:
Chapter 4. Self-refining consensus
Page 149 and 150:
- What do we want to measure? Chapt
Page 151 and 152:
φ (NMI) φ (NMI) φ (NMI) φ (NMI)
Page 153 and 154:
Page 155 and 156:
Page 157 and 158:
φ (NMI) 0.8 0.78 0.76 0.74 0.72 0
Page 159 and 160:
1. Given a cluster ensemble E conta
Page 161 and 162:
Page 163 and 164:
%of experiments relative % φ (NMI)
Page 165 and 166:
Page 167:
Publisher: Springer Series: Lecture
Page 170 and 171:
5.1. Generation of multimodal clust
Page 172 and 173:
5.2. Self-refining multimodal conse
Page 174 and 175:
5.3. Multimodal consensus clusterin
Page 176 and 177:
Page 178 and 179:
Page 180 and 181:
Page 182 and 183:
Page 184 and 185:
Page 186 and 187:
Page 188 and 189:
Page 190 and 191:
Page 192 and 193:
Page 194 and 195:
Page 196 and 197:
5.4. Discussion Data set Relative
Page 198 and 199:
5.5. Related publications modes joi
Page 200 and 201:
Chapter 6. Voting based consensus f
Page 202 and 203:
6.2. Adapting consensus functions t
Page 204 and 205:
Page 206 and 207:
Page 208 and 209:
6.3. Voting based consensus functio
Page 210 and 211:
Page 212 and 213:
Page 214 and 215:
Page 216 and 217:
Page 218 and 219:
Page 220 and 221:
6.4. Experiments λc = 1 1 1 3 3 3
Page 222 and 223:
6.4. Experiments Data set Soft clus
Page 224 and 225:
6.4. Experiments CSPA EAC HGPA MCLA
Page 226 and 227:
6.5. Discussion solutions on hard c
Page 229 and 230:
Chapter 7 Conclusions The contribut
Page 231 and 232:
Chapter 7. Conclusions of-the-art c
Page 233 and 234:
Chapter 7. Conclusions Though put f
Page 235 and 236:
Chapter 7. Conclusions clustering r
Page 237 and 238:
Chapter 7. Conclusions hardened. Ou
Page 239 and 240:
References Ben-Hur, A., D. Horn, H.
Page 241 and 242:
References Deerwester, S., S.-T. Du
Page 243 and 244:
References Fred, A. and A.K. Jain.
Page 245 and 246:
References Ingaramo, D., D. Pinto,
Page 247 and 248:
References Li, S.Z. and G. GuoDong.
Page 249 and 250:
References Sebastiani, F. 2002. Mac
Page 251 and 252:
References Topchy, A., A.K. Jain, a
Page 253 and 254:
Appendix A Experimental setup A.1 T
Page 255 and 256:
Appendix A. Experimental setup g. s
Page 257 and 258:
A.2.1 Unimodal data sets Appendix A
Page 259 and 260:
A.2.2 Multimodal data sets Appendix
Page 261 and 262:
Appendix A. Experimental setup gene
Page 263 and 264:
Appendix A. Experimental setup orig
Page 265 and 266:
Appendix A. Experimental setup Data
Page 267:
Appendix A. Experimental setup or N
Page 270 and 271:
B.1. Clustering indeterminacies in
Page 272 and 273:
Page 274 and 275:
Page 276 and 277:
Page 278 and 279:
Page 280 and 281:
Page 282 and 283:
Page 284 and 285:
Page 286 and 287:
C.1. Configuration of a random hier
Page 288 and 289:
C.2. Estimation of the computationa
Page 290 and 291:
Page 292 and 293:
Page 294 and 295:
Page 296 and 297:
Page 298 and 299:
Page 300 and 301:
Page 302 and 303:
Page 304 and 305:
Page 306 and 307:
Page 308 and 309:
Page 310 and 311:
Page 312 and 313:
Page 314 and 315:
Page 316 and 317:
Page 318 and 319:
Page 320 and 321:
Page 322 and 323:
Page 324 and 325:
Page 326 and 327:
C.4. Computationally optimal RHCA,
Page 328 and 329:
Page 330 and 331:
Page 332 and 333:
Page 334 and 335:
Page 336 and 337:
Page 338 and 339:
Page 340 and 341:
Page 342 and 343:
Page 344 and 345:
Page 346 and 347:
Page 348 and 349:
Page 350 and 351:
Page 352 and 353:
Page 354 and 355:
Page 356 and 357:
Page 358 and 359:
Page 360 and 361:
Page 362 and 363:
Page 364 and 365:
Page 366 and 367:
Page 368 and 369:
Page 370 and 371:
D.1. Experiments on consensus-based
Page 372 and 373:
Page 374 and 375:
Page 376 and 377:
Page 378 and 379:
Page 380 and 381:
Page 382 and 383:
Page 384 and 385:
D.2. Experiments on selection-based
Page 386 and 387:
Page 388 and 389:
Page 390 and 391:
Page 392 and 393:
Page 394 and 395:
Page 396 and 397:
E.1. CAL500 data set φ (NMI) 1 0.8
Page 398 and 399:
E.1. CAL500 data set φ (NMI) CSPA
Page 400 and 401:
E.2. InternetAds data set φ (NMI)
Page 402 and 403:
E.3. Corel data set φ (NMI) 1 0.8
Page 404 and 405:
Page 406 and 407:
Page 408 and 409:
Page 410 and 411:
F.1. Iris data set φ (NMI) 1 0.8 0
Page 412 and 413:
F.3. Glass data set CSPA EAC HGPA M
Page 414 and 415:
F.5. WDBC data set φ (NMI) 1 0.8 0
Page 416 and 417:
F.7. MFeat data set φ (NMI) 1 0.8
Page 418 and 419:
F.9. Segmentation data set φ (NMI)
Page 420 and 421:
F.10. BBC data set φ (NMI) 1 0.8 0
Page 422 and 423:
F.11. PenDigits data set φ (NMI) 1
show all

TESI DOCTORAL - La Salle

You also want an ePaper? Increase the reach of your titles

Delete template?

Save as template?