TESI DOCTORAL - La Salle

More documents

Recommendations

Info

A.6. Computational resources hierarchical clustering algorithm on it. The main difference between our implementation and the original one lies in the fact that we cut the resulting dendrogram at the desired number of clusters k, whereas Fred proposes performing the cut at the highest lifetime level, so that the very consensus function finds the natural number of clusters in the data set. Its computational complexity is O n 2 l (Fred and Jain, 2005). – ALSAD (Average-Link on Similarity As Data): this is one of the three consensus functions presented in (Kuncheva, Hadjitodorov, and Todorova, 2006) based on considering object similarity measures as object features. Despite the authors do not give a specific name to this family of consensus functions, we have named them xxSAD so as to indicate that similarities are deemed as data, replacing xx by the acronym of the particular clustering algorithm used for obtaining the consensus clustering solution. In this case, the pairwise object co-association matrix is partitioned using the average-link (AL) hierarchical clustering algorithm, cutting the resulting dendrogram at the desired number of clusters. Its computational complexity is O n 2 l for creating the object similarity matrix plus O n 2 for partitioning it with the hierarchical AL clustering algorithm (Xu and Wunsch II, 2005). – KMSAD (K-Means on Similarity As Data): this consensus function belongs to the same family as the previous one. This time, the object co-association matrix is clustered using the classic k-means (KM) partitional algorithm. Its computational complexity is O n 2 l for creating the object similarity matrix plus O (tkm) for partitioning it with the k-means clustering algorithm (Xu and Wunsch II, 2005) —where t is the number of iterations of k-means. – SLSAD (Single-Link on Similarity As Data): following the same approach as the AL- SAD and KMSAD consensus functions, the pairwise object co-association matrix is partitioned by means of the single-link (SL) hierarchical clustering algorithm in this case. As in the ALSAD consensus function, the consensus clustering solution is obtained by cutting the dendrogram at the desired number of clusters. Its computational costisthesameasALSAD. – VMA (Voting Merging Algorithm): this consensus function is based on sequentially solving the cluster correspondence problem on pairs of cluster ensemble components, and, at each iteration, applying a weighted version of the sum rule confidence voting method. This algorithm scales linearly in the number of objects in the data set and the number of cluster ensemble components, i.e. its complexity is O (nl) (Dimitriadou, Weingessel, and Hornik, 2002). A.6 Computational resources All the experiments conducted in this thesis have been executed under Matlab 7.0.4 on Dual Pentium 4 3GHz/1 GB RAM computers. The reason for choosing Matlab as the programming language for codifying our proposals is threefold: besides the fact we are familiar with it, the existence of multiple built-in functions simplifies the implementation of many of the processes involved in our proposals (Principal Component Analysis and Random Projection feature extraction, for instance). Moreover, the availability of the full Matlab source code of several components of our proposals (e.g. hypergraph consensus functions 230
Appendix A. Experimental setup or Non-negative Matrix Factorization feature extraction) has been a further incentive for this decision. However, the downside of this choice is the relatively slow execution of our proposals implementation, due to the fact that Matlab is an interpreted programming language. 231
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: 6.3. Voting based consensus functio
Page 218 and 219: 6.3. Voting based consensus functio
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: Appendix A. Experimental setup Data
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 316 and 317:
C.3. Estimation of the computationa
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?