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4.5. Related publications Year: 2006 Abstract: The performance of document clustering systems depends on employing optimal text representations, which are not only difficult to determine beforehand, but also may vary from one clustering problem to another. As a first step towards building robust document clusterers, a strategy based on feature diversity and cluster ensembles is presented in this work. Experiments conducted on a binary clustering problem show that our method is robust to near-optimal model order selection and able to detect constructive interactions between different document representations in the test bed. A subsequent extension of this work was published at the Journal of the Spanish Society for Natural Language Processing (Procesamiento del Lenguaje Natural) after its presentation at the SEPLN 2006 conference held at Zaragoza. Authors: Xavier Sevillano, Germán Cobo, Francesc Alías and Joan Claudi Socoró Title: Robust Document Clustering by Exploiting Feature Diversity in Cluster Ensembles In: Journal of the Spanish Society for Natural Language Processing (Procesamiento del Lenguaje Natural) Volume: 37 Pages: 169176 Year: 2006 Abstract: The performance of document clustering systems is conditioned by the use of optimal text representations, which are not only difficult to determine beforehand, but also may vary from one clustering problem to another. This work presents an approach based on feature diversity and cluster ensembles as a first step towards building document clustering systems that behave robustly across different clustering problems. Experiments conducted on three binary clustering problems of increasing difficulty show that the proposed method is i) robust to near-optimal model order selection, and ii) able to detect constructive interactions between different document representations, thus being capable of yielding consensus clusterings superior to any of the individual clusterings available. Last, a global analysis regarding clustering indeterminacies and how they can be overcome via cluster ensembles was presented at the ICA 2007 conference as an oral presentation. Authors: Xavier Sevillano, Germán Cobo, Francesc Alías and Joan Claudi Socoró Title: Text Clustering on Latent Thematic Spaces: Variants, Strenghts and Weaknesses In: Proceedings of 7th International Conference on Independent Component Analysis and Signal Separation 130
Publisher: Springer Series: Lecture Notes in Computer Science Volume: 4666 Chapter 4. Self-refining consensus architectures Editors: Mike E. Davies, Christopher J. James, Samer A. Abdallah and Mark D. Plumbley Pages: 794-801 Year: 2007 Abstract: Deriving a thematically meaningful partition of an unlabeled document corpus is a challenging task. In this context, the use of document representations based on latent thematic generative models can lead to improved clustering. However, determining apriorithe optimal document indexing technique is not straighforward, as it depends on the clustering problem faced and the partitioning strategy adopted. So as to overcome this indeterminacy, we propose deriving a single consensus labeling upon the results of clustering processes executed on several document representations. Experiments conducted on subsets of two standard text corpora evaluate distinct clustering strategies based on latent thematic spaces and highlight the usefulness of consensus clustering to overcome the indeterminacy regarding optimal document indexing. 131
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C.I.F. G: 59069740 Universitat Ramo
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Resum En segmentar de forma no supe
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Abstract When facing the task of pa
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Contents 2.2.6 Consensus functions
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Contents A.5 Consensus functions .
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Contents D.2.5 WDBC data set . . .
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List of Tables 3.13 Relative percen
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List of Tables 5.15 Relative φ (NM
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List of Figures 1.1 Evolution of th
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List of Figures 4.2 Decreasingly or
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List of Figures C.18 Estimated and
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List of Figures C.49 φ (NMI) of th
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List of Figures D.22 φ (NMI) boxpl
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List of Algorithms 6.1 Symbolic des
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List of symbols OΛ: object co-asso
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Chapter 1. Framework of the thesis
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1.1. Knowledge discovery and data m
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1.1. Knowledge discovery and data m
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1.2. Clustering in knowledge discov
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1.3. Multimodal clustering in clust
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1.4. Clustering indeterminacies exe
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1.4. Clustering indeterminacies The
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1.4. Clustering indeterminacies Dat
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1.5. Motivation and contributions o
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Chapter 2. Cluster ensembles and co
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fuzzy consensus clustering solution
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2.1. Related work on cluster ensemb
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2.2. Related work on consensus func
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3.1. Motivation - the computational
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3.1. Motivation An additional and v
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3.2. Random hierarchical consensus
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3.3. Deterministic hierarchical con
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Page 147 and 148: Chapter 4. Self-refining consensus
Page 149 and 150: - What do we want to measure? Chapt
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Page 157 and 158: φ (NMI) 0.8 0.78 0.76 0.74 0.72 0
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Page 202 and 203: 6.2. Adapting consensus functions t
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6.3. Voting based consensus functio
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6.3. Voting based consensus functio
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6.4. Experiments λc = 1 1 1 3 3 3
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6.4. Experiments Data set Soft clus
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6.4. Experiments CSPA EAC HGPA MCLA
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6.5. Discussion solutions on hard c
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Chapter 7 Conclusions The contribut
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Chapter 7. Conclusions of-the-art c
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Chapter 7. Conclusions Though put f
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Chapter 7. Conclusions clustering r
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Chapter 7. Conclusions hardened. Ou
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References Ben-Hur, A., D. Horn, H.
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References Deerwester, S., S.-T. Du
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References Fred, A. and A.K. Jain.
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References Ingaramo, D., D. Pinto,
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References Li, S.Z. and G. GuoDong.
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References Sebastiani, F. 2002. Mac
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References Topchy, A., A.K. Jain, a
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Appendix A Experimental setup A.1 T
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Appendix A. Experimental setup g. s
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A.2.1 Unimodal data sets Appendix A
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A.2.2 Multimodal data sets Appendix
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Appendix A. Experimental setup gene
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Appendix A. Experimental setup orig
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Appendix A. Experimental setup Data
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Appendix A. Experimental setup or N
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B.1. Clustering indeterminacies in
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C.1. Configuration of a random hier
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C.2. Estimation of the computationa
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C.4. Computationally optimal RHCA,
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D.1. Experiments on consensus-based
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D.2. Experiments on selection-based
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E.1. CAL500 data set φ (NMI) 1 0.8
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E.1. CAL500 data set φ (NMI) CSPA
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E.2. InternetAds data set φ (NMI)
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E.3. Corel data set φ (NMI) 1 0.8
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F.1. Iris data set φ (NMI) 1 0.8 0
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F.3. Glass data set CSPA EAC HGPA M
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F.5. WDBC data set φ (NMI) 1 0.8 0
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F.7. MFeat data set φ (NMI) 1 0.8
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F.9. Segmentation data set φ (NMI)
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F.10. BBC data set φ (NMI) 1 0.8 0
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F.11. PenDigits data set φ (NMI) 1
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