Volume

Dagstuhl Seminar Proceedings, Volume 7131

Publication Details

  • published at: 2007-07-16
  • Publisher: Schloss Dagstuhl – Leibniz-Zentrum für Informatik

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Document
DOI: 10.4230/DagSemProc.07131.1
07131 Abstracts Collection – Similarity-based Clustering and its Application to Medicine and Biology

Authors: Michael Biehl, Barbara Hammer, Michel Verleysen, and Thomas Villmann

Abstract
From 25.03. to 30.03.2007, the Dagstuhl Seminar 07131 ``Similarity-based Clustering and its Application to Medicine and Biology'' was held in the International Conference and Research Center (IBFI), Schloss Dagstuhl. During the seminar, several participants presented their current research, and ongoing work and open problems were discussed. Abstracts of the presentations given during the seminar as well as abstracts of seminar results and ideas are put together in this paper. The first section describes the seminar topics and goals in general. Links to extended abstracts or full papers are provided, if available.
Cite as

Michael Biehl, Barbara Hammer, Michel Verleysen, and Thomas Villmann. 07131 Abstracts Collection – Similarity-based Clustering and its Application to Medicine and Biology. In Similarity-based Clustering and its Application to Medicine and Biology. Dagstuhl Seminar Proceedings, Volume 7131, pp. 1-12, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2007)

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@InProceedings{biehl_et_al:DagSemProc.07131.1,
  author =	{Biehl, Michael and Hammer, Barbara and Verleysen, Michel and Villmann, Thomas},
  title =	{{07131 Abstracts Collection – Similarity-based Clustering and its Application to Medicine and Biology}},
  booktitle =	{Similarity-based Clustering and its Application to Medicine and Biology},
  pages =	{1--12},
  series =	{Dagstuhl Seminar Proceedings (DagSemProc)},
  ISSN =	{1862-4405},
  year =	{2007},
  volume =	{7131},
  editor =	{Michael Biehl and Barbara Hammer and Michel Verleysen and Thomas Villmann},
  publisher =	{Schloss Dagstuhl -- Leibniz-Zentrum f{\"u}r Informatik},
  address =	{Dagstuhl, Germany},
  URL =		{https://drops.dagstuhl.de/entities/document/10.4230/DagSemProc.07131.1},
  URN =		{urn:nbn:de:0030-drops-11190},
  doi =		{10.4230/DagSemProc.07131.1},
  annote =	{Keywords: Similarity-based clustering and classification, prototype-based classifiers, self-organisation, SOM, learning vector quantization, medical diagnosis, bioinformatics}
}
Document
DOI: 10.4230/DagSemProc.07131.2
07131 Summary – Similarity-based Clustering and its Application to Medicine and Biology

Authors: Michael Biehl, Barbara Hammer, Michel Verleysen, and Thomas Villmann

Abstract
This paper summarizes presentations, discussions, and results of the Dagstuhl seminar.
Cite as

Michael Biehl, Barbara Hammer, Michel Verleysen, and Thomas Villmann. 07131 Summary – Similarity-based Clustering and its Application to Medicine and Biology. In Similarity-based Clustering and its Application to Medicine and Biology. Dagstuhl Seminar Proceedings, Volume 7131, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2007)

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@InProceedings{biehl_et_al:DagSemProc.07131.2,
  author =	{Biehl, Michael and Hammer, Barbara and Verleysen, Michel and Villmann, Thomas},
  title =	{{07131 Summary – Similarity-based Clustering and its Application to Medicine and Biology}},
  booktitle =	{Similarity-based Clustering and its Application to Medicine and Biology},
  series =	{Dagstuhl Seminar Proceedings (DagSemProc)},
  ISSN =	{1862-4405},
  year =	{2007},
  volume =	{7131},
  editor =	{Michael Biehl and Barbara Hammer and Michel Verleysen and Thomas Villmann},
  publisher =	{Schloss Dagstuhl -- Leibniz-Zentrum f{\"u}r Informatik},
  address =	{Dagstuhl, Germany},
  URL =		{https://drops.dagstuhl.de/entities/document/10.4230/DagSemProc.07131.2},
  URN =		{urn:nbn:de:0030-drops-11177},
  doi =		{10.4230/DagSemProc.07131.2},
  annote =	{Keywords: Clustering, bioinformatics, medicine}
}
Document
DOI: 10.4230/DagSemProc.07131.3
Advances in pre-processing and model generation for mass spectrometric data analysis

Authors: Frank Michael Schleif

Abstract
The analysis of complex signals as obtained by mass spectrometric measurements is complicated and needs an appropriate representation of the data. Thereby the kind of preprocessing, feature extraction as well as the used similarity measure are of particular importance. Focusing on biomarker analysis and taking the functional nature of the data into account this task is even more complicated. A new mass spectrometry tailored data preprocessing is shown, discussed and analyzed in a clinical proteom study compared to a standard setting.
Cite as

Frank Michael Schleif. Advances in pre-processing and model generation for mass spectrometric data analysis. In Similarity-based Clustering and its Application to Medicine and Biology. Dagstuhl Seminar Proceedings, Volume 7131, pp. 1-24, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2007)

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@InProceedings{schleif:DagSemProc.07131.3,
  author =	{Schleif, Frank Michael},
  title =	{{Advances in pre-processing and model generation for mass spectrometric data analysis}},
  booktitle =	{Similarity-based Clustering and its Application to Medicine and Biology},
  pages =	{1--24},
  series =	{Dagstuhl Seminar Proceedings (DagSemProc)},
  ISSN =	{1862-4405},
  year =	{2007},
  volume =	{7131},
  editor =	{Michael Biehl and Barbara Hammer and Michel Verleysen and Thomas Villmann},
  publisher =	{Schloss Dagstuhl -- Leibniz-Zentrum f{\"u}r Informatik},
  address =	{Dagstuhl, Germany},
  URL =		{https://drops.dagstuhl.de/entities/document/10.4230/DagSemProc.07131.3},
  URN =		{urn:nbn:de:0030-drops-11329},
  doi =		{10.4230/DagSemProc.07131.3},
  annote =	{Keywords: Similarity measures, functional data, proteomics, mass spectrometry, pre-processing,wavelet analysis, generalized peak list}
}
Document
DOI: 10.4230/DagSemProc.07131.4
Correlation-based Data Representation

Authors: Marc Strickert and Udo Seiffert

Abstract
The Dagstuhl Seminar 'Similarity-based Clustering and its Application to Medicine and Biology' (07131) held in March 25--30, 2007, provided an excellent atmosphere for in-depth discussions about the research frontier of computational methods for relevant applications of biomedical clustering and beyond. We address some highlighted issues about correlation-based data analysis in this seminar postribution. First, some prominent correlation measures are briefly revisited. Then, a focus is put on Pearson correlation, because of its widespread use in biomedical sciences and because of its analytic accessibility. A connection to Euclidean distance of z-score transformed data outlined. Cost function optimization of correlation-based data representation is discussed for which, finally, applications to visualization and clustering of gene expression data are given.
Cite as

Marc Strickert and Udo Seiffert. Correlation-based Data Representation. In Similarity-based Clustering and its Application to Medicine and Biology. Dagstuhl Seminar Proceedings, Volume 7131, pp. 1-16, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2007)

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@InProceedings{strickert_et_al:DagSemProc.07131.4,
  author =	{Strickert, Marc and Seiffert, Udo},
  title =	{{Correlation-based Data Representation}},
  booktitle =	{Similarity-based Clustering and its Application to Medicine and Biology},
  pages =	{1--16},
  series =	{Dagstuhl Seminar Proceedings (DagSemProc)},
  ISSN =	{1862-4405},
  year =	{2007},
  volume =	{7131},
  editor =	{Michael Biehl and Barbara Hammer and Michel Verleysen and Thomas Villmann},
  publisher =	{Schloss Dagstuhl -- Leibniz-Zentrum f{\"u}r Informatik},
  address =	{Dagstuhl, Germany},
  URL =		{https://drops.dagstuhl.de/entities/document/10.4230/DagSemProc.07131.4},
  URN =		{urn:nbn:de:0030-drops-11347},
  doi =		{10.4230/DagSemProc.07131.4},
  annote =	{Keywords: Correlation, data representation, gradient-based optimization, clustering, neural gas}
}
Document
DOI: 10.4230/DagSemProc.07131.5
Learning Vector Quantization: generalization ability and dynamics of competing prototypes

Authors: Aree Witoelar, Michael Biehl, and Barbara Hammer

Abstract
Learning Vector Quantization (LVQ) are popular multi-class classification algorithms. Prototypes in an LVQ system represent the typical features of classes in the data. Frequently multiple prototypes are employed for a class to improve the representation of variations within the class and the generalization ability. In this paper, we investigate the dynamics of LVQ in an exact mathematical way, aiming at understanding the influence of the number of prototypes and their assignment to classes. The theory of on-line learning allows a mathematical description of the learning dynamics in model situations. We demonstrate using a system of three prototypes the different behaviors of LVQ systems of multiple prototype and single prototype class representation.
Cite as

Aree Witoelar, Michael Biehl, and Barbara Hammer. Learning Vector Quantization: generalization ability and dynamics of competing prototypes. In Similarity-based Clustering and its Application to Medicine and Biology. Dagstuhl Seminar Proceedings, Volume 7131, pp. 1-11, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2007)

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@InProceedings{witoelar_et_al:DagSemProc.07131.5,
  author =	{Witoelar, Aree and Biehl, Michael and Hammer, Barbara},
  title =	{{Learning Vector Quantization: generalization ability and dynamics of competing prototypes}},
  booktitle =	{Similarity-based Clustering and its Application to Medicine and Biology},
  pages =	{1--11},
  series =	{Dagstuhl Seminar Proceedings (DagSemProc)},
  ISSN =	{1862-4405},
  year =	{2007},
  volume =	{7131},
  editor =	{Michael Biehl and Barbara Hammer and Michel Verleysen and Thomas Villmann},
  publisher =	{Schloss Dagstuhl -- Leibniz-Zentrum f{\"u}r Informatik},
  address =	{Dagstuhl, Germany},
  URL =		{https://drops.dagstuhl.de/entities/document/10.4230/DagSemProc.07131.5},
  URN =		{urn:nbn:de:0030-drops-11311},
  doi =		{10.4230/DagSemProc.07131.5},
  annote =	{Keywords: Online learning, learning vector quantization}
}
Document
DOI: 10.4230/DagSemProc.07131.6
Relational Clustering

Authors: Barbara Hammer and Alexander Hasenfuss

Abstract
We introduce relational variants of neural gas, a very efficient and powerful neural clustering algorithm. It is assumed that a similarity or dissimilarity matrix is given which stems from Euclidean distance or dot product, respectively, however, the underlying embedding of points is unknown. In this case, one can equivalently formulate batch optimization in terms of the given similarities or dissimilarities, thus providing a way to transfer batch optimization to relational data. Interestingly, convergence is guaranteed even for general symmetric and nonsingular metrics.
Cite as

Barbara Hammer and Alexander Hasenfuss. Relational Clustering. In Similarity-based Clustering and its Application to Medicine and Biology. Dagstuhl Seminar Proceedings, Volume 7131, pp. 1-15, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2007)

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@InProceedings{hammer_et_al:DagSemProc.07131.6,
  author =	{Hammer, Barbara and Hasenfuss, Alexander},
  title =	{{Relational Clustering}},
  booktitle =	{Similarity-based Clustering and its Application to Medicine and Biology},
  pages =	{1--15},
  series =	{Dagstuhl Seminar Proceedings (DagSemProc)},
  ISSN =	{1862-4405},
  year =	{2007},
  volume =	{7131},
  editor =	{Michael Biehl and Barbara Hammer and Michel Verleysen and Thomas Villmann},
  publisher =	{Schloss Dagstuhl -- Leibniz-Zentrum f{\"u}r Informatik},
  address =	{Dagstuhl, Germany},
  URL =		{https://drops.dagstuhl.de/entities/document/10.4230/DagSemProc.07131.6},
  URN =		{urn:nbn:de:0030-drops-11182},
  doi =		{10.4230/DagSemProc.07131.6},
  annote =	{Keywords: Neural gas, dissimilarity data}
}
Document
DOI: 10.4230/DagSemProc.07131.7
Relevance Matrices in LVQ

Authors: Petra Schneider

Abstract
LVQ-networks belong to the class of distance-based classifiers. The underlying distance measure is of special importance for their performance, because it defines how the data items are compared and how they are grouped in clusters. Relevance Learning techniques try to adapt the distance measure to the specific data used for training. I will present a new adaptive distance measure in Learning Vector Quantization which is an extension of previously proposed Relevance Learning schemes. In comparison to the already existing techniques for Relevance Learning, this distance measure is more powerful to represent the internal structure of the data appropriately. Two applications will be used to demonstrate the behavior of the new algorithm (artificial and real life).
Cite as

Petra Schneider. Relevance Matrices in LVQ. In Similarity-based Clustering and its Application to Medicine and Biology. Dagstuhl Seminar Proceedings, Volume 7131, pp. 1-6, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2007)

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@InProceedings{schneider:DagSemProc.07131.7,
  author =	{Schneider, Petra},
  title =	{{Relevance Matrices in LVQ}},
  booktitle =	{Similarity-based Clustering and its Application to Medicine and Biology},
  pages =	{1--6},
  series =	{Dagstuhl Seminar Proceedings (DagSemProc)},
  ISSN =	{1862-4405},
  year =	{2007},
  volume =	{7131},
  editor =	{Michael Biehl and Barbara Hammer and Michel Verleysen and Thomas Villmann},
  publisher =	{Schloss Dagstuhl -- Leibniz-Zentrum f{\"u}r Informatik},
  address =	{Dagstuhl, Germany},
  URL =		{https://drops.dagstuhl.de/entities/document/10.4230/DagSemProc.07131.7},
  URN =		{urn:nbn:de:0030-drops-11332},
  doi =		{10.4230/DagSemProc.07131.7},
  annote =	{Keywords: Learning Vector Quantization, Relevance Learning, adaptive distance measure}
}

Filters

  • Authors
  • B
  • Biehl, Michael
  • H
  • Hammer, Barbara
  • Hasenfuss, Alexander
  • S
  • Schleif, Frank Michael
  • Schneider, Petra
  • Seiffert, Udo
  • Strickert, Marc
  • V
  • Verleysen, Michel
  • Villmann, Thomas
  • W
  • Witoelar, Aree

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