TESI DOCTORAL - La Salle

More documents

Recommendations

Info

C.2. Estimation of the computationally optimal RHCA sets, our proposed method allows obtaining a pretty accurate estimation of the execution time of any serial RHCA variant which, at the same time, allows the user to make a reliable decision regarding the most computationally efficient consensus architecture whatever the diversity scenario and consensus function is employed. Secondly, figure C.12 presents the corresponding magnitudes in the case that the fully parallel implementations of RHCA are employed. In this situation, the estimation of the real execution time is not as accurate as in the serial case, although the running time deviation suffered when a suboptimal RHCA architecture is selected is, from a practical viewpoint, perfectly assumable — a fact that has already been reported in the previous data sets. C.2.7 MFeat data set In this section, the results of estimating the execution times of RHCA are compared to their real counterparts across four diversity scenarios in the context of the MFeat data collection. The cluster ensemble sizes corresponding to these diversity scenarios are l =6, 60, 114 and 168, respectively. Figure C.13 presents the estimated and real running times of multiple variants of the serial implementation of RHCA on this data set. Besides the notably high accuracy of the estimation, we would like to highlight that flat consensus turns out to be the most efficient consensus architecture in the four diversity scenarios for all but two of the consensus functions employed (MCLA and HGPA), a behaviour that has already been observed in other data collections with small cluster ensembles (e.g. the Iris data set). The results corresponding to the parallel implementation of RHCA are depicted in figure C.14. In this case, the use of the HGPA and MCLA consensus functions as clustering combiners also make the RHCA variants with s =2ands = 3 stages computationally optimal. However, for the remaining consensus functions, flat consensus mostly prevails as the most efficient consensus architecture in most diversity scenarios. 266
SERT RHCA (sec.) SERT RHCA (sec.) SERT RHCA (sec.) SERT RHCA (sec.) Appendix C. Experiments on hierarchical consensus architectures 2 10 2 1 2 s : number of stages 10 1 10 0 10 −1 10 3 10 2 10 1 10 0 10 3 10 2 10 1 10 0 10 3 10 2 10 1 2 3 7 b : mini−ensemble size (a) Estimated RT, |dfA| =1 s : number of stages 6 4 3 3 2 2 1 2 3 4 6 7 35 70 b : mini−ensemble size (c) Estimated RT, |dfA| =10 s : number of stages 7 5 4 3 3 2 2 1 2 3 4 5 9 10 66 133 b : mini−ensemble size (e) Estimated RT, |dfA| =19 s : number of stages 7 5 4 4 3 3 2 2 1 10 2 3 4 5 6 11 12 98 196 0 b : mini−ensemble size (g) Estimated RT, |dfA| =28 CSPA EAC HGPA MCLA ALSAD KMSAD SLSAD CSPA EAC HGPA MCLA ALSAD KMSAD SLSAD CSPA EAC HGPA MCLA ALSAD KMSAD SLSAD CSPA EAC HGPA MCLA ALSAD KMSAD SLSAD SRT RHCA (sec.) SRT RHCA (sec.) SRT RHCA (sec.) SRT RHCA (sec.) 2 10 2 1 2 s : number of stages 10 1 10 0 10 −1 10 3 10 2 10 1 10 0 10 3 10 2 10 1 10 0 10 3 10 2 10 1 2 3 7 b : mini−ensemble size (b) Real RT, |dfA| =1 s : number of stages 6 4 3 3 2 2 1 2 3 4 6 7 35 70 b : mini−ensemble size (d) Real RT, |dfA| =10 s : number of stages 7 5 4 3 3 2 2 1 2 3 4 5 9 10 66 133 b : mini−ensemble size (f) Real RT, |dfA| =19 s : number of stages 7 5 4 4 3 3 2 2 1 10 2 3 4 5 6 11 12 98 196 0 b : mini−ensemble size (h) Real RT, |dfA| =28 Figure C.11: Estimated and real running times (RT) of the serial RHCA implementation on the Balance data collection in the four diversity scenarios |dfA| = {1, 10, 19, 28}. 267 CSPA EAC HGPA MCLA ALSAD KMSAD SLSAD CSPA EAC HGPA MCLA ALSAD KMSAD SLSAD CSPA EAC HGPA MCLA ALSAD KMSAD SLSAD CSPA EAC HGPA MCLA ALSAD KMSAD SLSAD
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 46 and 47:
Page 48 and 49:
Page 50 and 51:
Page 52 and 53:
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 72 and 73:
Page 74 and 75:
Page 76 and 77:
Page 78 and 79:
Page 80 and 81:
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 88 and 89:
Page 90 and 91:
Page 92 and 93:
Page 94 and 95:
Page 96 and 97:
Page 98 and 99:
Page 100 and 101:
Page 102 and 103:
Page 104 and 105:
Page 106 and 107:
3.3. Deterministic hierarchical con
Page 108 and 109:
Page 110 and 111:
Page 112 and 113:
Page 114 and 115:
Page 116 and 117:
Page 118 and 119:
Page 120 and 121:
Page 122 and 123:
Page 124 and 125:
3.4. Flat vs. hierarchical consensu
Page 126 and 127:
Page 128 and 129:
Page 130 and 131:
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 286 and 287: C.1. Configuration of a random hier
Page 288 and 289: C.2. Estimation of the computationa
Page 326 and 327: C.4. Computationally optimal RHCA,
Page 352 and 353:
C.4. Computationally optimal RHCA,
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

Create successful ePaper yourself

Delete template?

Save as template?