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Dagstuhl Seminar Proceedings, Volume 7181

Publication Details

  • published at: 2007-12-11
  • Publisher: Schloss Dagstuhl – Leibniz-Zentrum für Informatik

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Document
DOI: 10.4230/DagSemProc.07181.1
07181 Abstracts Collection – Parallel Universes and Local Patterns

Authors: Michael R. Berthold, Katharina Morik, and Arno Siebes

Abstract
From 1 May 2007 to 4 May 2007 the Dagstuhl Seminar 07181 ``Parallel Universes and Local Patterns'' 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 R. Berthold, Katharina Morik, and Arno Siebes. 07181 Abstracts Collection – Parallel Universes and Local Patterns. In Parallel Universes and Local Patterns. Dagstuhl Seminar Proceedings, Volume 7181, pp. 1-15, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2007)

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@InProceedings{berthold_et_al:DagSemProc.07181.1,
  author =	{Berthold, Michael R. and Morik, Katharina and Siebes, Arno},
  title =	{{07181 Abstracts Collection – Parallel Universes and Local Patterns }},
  booktitle =	{Parallel Universes and Local Patterns},
  pages =	{1--15},
  series =	{Dagstuhl Seminar Proceedings (DagSemProc)},
  ISSN =	{1862-4405},
  year =	{2007},
  volume =	{7181},
  editor =	{Michael R. Berthold and Katharina Morik and Arno Siebes},
  publisher =	{Schloss Dagstuhl -- Leibniz-Zentrum f{\"u}r Informatik},
  address =	{Dagstuhl, Germany},
  URL =		{https://drops.dagstuhl.de/entities/document/10.4230/DagSemProc.07181.1},
  URN =		{urn:nbn:de:0030-drops-12662},
  doi =		{10.4230/DagSemProc.07181.1},
  annote =	{Keywords: Local Patterns, Global Models, Parallel Universes, Descriptor Spaces}
}
Document
DOI: 10.4230/DagSemProc.07181.2
07181 Introduction – Parallel Universes and Local Patterns

Authors: Michael R. Berthold, Katharina Morik, and Arno Siebes

Abstract
Learning in parallel universes and the mining for local patterns are both relatively new fields of research. Local pattern detection addresses the problem of identifying (small) deviations from an overall distribution of some underlying data in some feature space. Learning in parallel universes on the other hand, deals with the analysis of objects, which are given in different feature spaces, i.e.\ parallel universes; and the aim is on finding groups of objects, which show ``interesting'' behavior in some of these universes. So, while local patterns describe interesting properties of a subset of the overall space or set of objects, learning in parallel universes also aims at finding interesting patterns across different feature spaces or object descriptions. Dagstuhl Seminar~07181 on Parallel Universes and Local Patterns held in May 2007 brought together researchers with different backgrounds to discuss latest advances in both fields and to draw connections between the two.
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Michael R. Berthold, Katharina Morik, and Arno Siebes. 07181 Introduction – Parallel Universes and Local Patterns. In Parallel Universes and Local Patterns. Dagstuhl Seminar Proceedings, Volume 7181, pp. 1-4, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2007)

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@InProceedings{berthold_et_al:DagSemProc.07181.2,
  author =	{Berthold, Michael R. and Morik, Katharina and Siebes, Arno},
  title =	{{07181 Introduction – Parallel Universes and Local Patterns }},
  booktitle =	{Parallel Universes and Local Patterns},
  pages =	{1--4},
  series =	{Dagstuhl Seminar Proceedings (DagSemProc)},
  ISSN =	{1862-4405},
  year =	{2007},
  volume =	{7181},
  editor =	{Michael R. Berthold and Katharina Morik and Arno Siebes},
  publisher =	{Schloss Dagstuhl -- Leibniz-Zentrum f{\"u}r Informatik},
  address =	{Dagstuhl, Germany},
  URL =		{https://drops.dagstuhl.de/entities/document/10.4230/DagSemProc.07181.2},
  URN =		{urn:nbn:de:0030-drops-12655},
  doi =		{10.4230/DagSemProc.07181.2},
  annote =	{Keywords: Local Patterns, Global Models, Parallel Universes, Descriptor Spaces}
}
Document
DOI: 10.4230/DagSemProc.07181.3
Discovering Knowledge from Local Patterns with Global Constraints

Authors: Bruno Crémilleux and Arnaud Soulet

Abstract
It is well known that local patterns are at the core of a lot of knowledge which may be discovered from data. Nevertheless, use of local patterns is limited by their huge number and computational costs. Several approaches (e.g., condensed representations, pattern set discovery) aim at grouping or synthesizing local patterns to provide a global view of the data. A global pattern is a pattern which is a set or a synthesis of local patterns coming from the data. In this paper, we propose the idea of global constraints to write queries addressing global patterns. A key point is the ability to bias the designing of global patterns according to the expectation of the user. For instance, a global pattern can be oriented towards the search of exceptions or a clustering. It requires to write queries taking into account such biases. Open issues are to design a generic framework to express powerful global constraints and solvers to mine them. We think that global constraints are a promising way to discover relevant global patterns.
Cite as

Bruno Crémilleux and Arnaud Soulet. Discovering Knowledge from Local Patterns with Global Constraints. In Parallel Universes and Local Patterns. Dagstuhl Seminar Proceedings, Volume 7181, pp. 1-9, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2007)

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@InProceedings{cremilleux_et_al:DagSemProc.07181.3,
  author =	{Cr\'{e}milleux, Bruno and Soulet, Arnaud},
  title =	{{Discovering Knowledge from Local Patterns with Global Constraints}},
  booktitle =	{Parallel Universes and Local Patterns},
  pages =	{1--9},
  series =	{Dagstuhl Seminar Proceedings (DagSemProc)},
  ISSN =	{1862-4405},
  year =	{2007},
  volume =	{7181},
  editor =	{Michael R. Berthold and Katharina Morik and Arno Siebes},
  publisher =	{Schloss Dagstuhl -- Leibniz-Zentrum f{\"u}r Informatik},
  address =	{Dagstuhl, Germany},
  URL =		{https://drops.dagstuhl.de/entities/document/10.4230/DagSemProc.07181.3},
  URN =		{urn:nbn:de:0030-drops-12594},
  doi =		{10.4230/DagSemProc.07181.3},
  annote =	{Keywords: Local patterns, constraint-based paradigm, global constraints}
}
Document
DOI: 10.4230/DagSemProc.07181.4
Learning in Parallel Universes

Authors: Michael R. Berthold and Bernd Wiswedel

Abstract
This abstract summarizes a brief, preliminary formalization of learning in parallel universes. It also attempts to highlight a few neighboring learning paradigms to illustrate how parallel learning fits into the greater picture.
Cite as

Michael R. Berthold and Bernd Wiswedel. Learning in Parallel Universes. In Parallel Universes and Local Patterns. Dagstuhl Seminar Proceedings, Volume 7181, pp. 1-3, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2007)

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@InProceedings{berthold_et_al:DagSemProc.07181.4,
  author =	{Berthold, Michael R. and Wiswedel, Bernd},
  title =	{{Learning in Parallel Universes}},
  booktitle =	{Parallel Universes and Local Patterns},
  pages =	{1--3},
  series =	{Dagstuhl Seminar Proceedings (DagSemProc)},
  ISSN =	{1862-4405},
  year =	{2007},
  volume =	{7181},
  editor =	{Michael R. Berthold and Katharina Morik and Arno Siebes},
  publisher =	{Schloss Dagstuhl -- Leibniz-Zentrum f{\"u}r Informatik},
  address =	{Dagstuhl, Germany},
  URL =		{https://drops.dagstuhl.de/entities/document/10.4230/DagSemProc.07181.4},
  URN =		{urn:nbn:de:0030-drops-12586},
  doi =		{10.4230/DagSemProc.07181.4},
  annote =	{Keywords: Parallel universes}
}
Document
DOI: 10.4230/DagSemProc.07181.5
Multi-Aspect Tagging for Collaborative Structuring

Authors: Katharina Morik and Michael Wurst

Abstract
Local tag structures have become frequent though Web 2.0: Users "tag" their data without specifying the underlying semantics. A collection of media items is tagged multiply using different aspects, e.g., topic, genre, occasion, mood. Given the large number of local, individual structures, users could benefit from the tagging work of others ("folksonomies"). In contrast to distributed clustering, no global structure is wanted. Each user wants to keep the tags already annotated, wants to keep the diverse aspects under which the items were organized, and only wishes to enhance the own structure by those of others. A clustering algorithm which structures items has to take into account the local, multi-aspect nature of the task structures. The LACE algorithm (Wurst et al. 2006) is such a clustering algorithm.
Cite as

Katharina Morik and Michael Wurst. Multi-Aspect Tagging for Collaborative Structuring. In Parallel Universes and Local Patterns. Dagstuhl Seminar Proceedings, Volume 7181, pp. 1-5, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2007)

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@InProceedings{morik_et_al:DagSemProc.07181.5,
  author =	{Morik, Katharina and Wurst, Michael},
  title =	{{Multi-Aspect Tagging for Collaborative Structuring}},
  booktitle =	{Parallel Universes and Local Patterns},
  pages =	{1--5},
  series =	{Dagstuhl Seminar Proceedings (DagSemProc)},
  ISSN =	{1862-4405},
  year =	{2007},
  volume =	{7181},
  editor =	{Michael R. Berthold and Katharina Morik and Arno Siebes},
  publisher =	{Schloss Dagstuhl -- Leibniz-Zentrum f{\"u}r Informatik},
  address =	{Dagstuhl, Germany},
  URL =		{https://drops.dagstuhl.de/entities/document/10.4230/DagSemProc.07181.5},
  URN =		{urn:nbn:de:0030-drops-12635},
  doi =		{10.4230/DagSemProc.07181.5},
  annote =	{Keywords: Ensemble Clustering, automatic tagging, localized clustering}
}
Document
DOI: 10.4230/DagSemProc.07181.6
Note on parallel universes

Authors: Niall Adams and David J. Hand

Abstract
The parallel universes idea is an attempt to integrate several aspects of learning which share some common aspects. This is an interesting idea: if successful, insights could cross-fertilise, leading to advances in each area. The "multi-view" perspective seems to us to have particular potential.
Cite as

Niall Adams and David J. Hand. Note on parallel universes. In Parallel Universes and Local Patterns. Dagstuhl Seminar Proceedings, Volume 7181, pp. 1-2, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2007)

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@InProceedings{adams_et_al:DagSemProc.07181.6,
  author =	{Adams, Niall and Hand, David J.},
  title =	{{Note on parallel universes}},
  booktitle =	{Parallel Universes and Local Patterns},
  pages =	{1--2},
  series =	{Dagstuhl Seminar Proceedings (DagSemProc)},
  ISSN =	{1862-4405},
  year =	{2007},
  volume =	{7181},
  editor =	{Michael R. Berthold and Katharina Morik and Arno Siebes},
  publisher =	{Schloss Dagstuhl -- Leibniz-Zentrum f{\"u}r Informatik},
  address =	{Dagstuhl, Germany},
  URL =		{https://drops.dagstuhl.de/entities/document/10.4230/DagSemProc.07181.6},
  URN =		{urn:nbn:de:0030-drops-12565},
  doi =		{10.4230/DagSemProc.07181.6},
  annote =	{Keywords: Local patterns, fraud detection}
}
Document
DOI: 10.4230/DagSemProc.07181.7
Parallel universes to improve the diagnosis of cardiac arrhythmias

Authors: Elisa Fromont, René Quiniou, and Marie-Odile Cordier

Abstract
We are interested in using parallel universes to learn interpretable models that can be subsequently used to automatically diagnose cardiac arrythmias. In our study, parallel universes are heterogeneous sources such as electrocardiograms, blood pressure measurements, phonocardiograms etc. that give relevant information about the cardiac state of a patient. To learn interpretable rules, we use an inductive logic programming (ILP) method on a symbolic version of our data. Aggregating the symbolic data coming from all the sources before learning, increases both the number of possible relations that can be learned and the richness of the language. We propose a two-step strategy to deal with these dimensionality problems when using ILP. First, rules are learned independently in each universe. Second, the learned rules are used to bias a new learning process from the aggregated data. The results show that this method is much more efficient than learning directly from the aggregated data. Furthermore the good accuracy results confirm the benefits of using multiple sources when trying to improve the diagnosis of cardiac arrythmias.
Cite as

Elisa Fromont, René Quiniou, and Marie-Odile Cordier. Parallel universes to improve the diagnosis of cardiac arrhythmias. In Parallel Universes and Local Patterns. Dagstuhl Seminar Proceedings, Volume 7181, pp. 1-4, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2007)

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@InProceedings{fromont_et_al:DagSemProc.07181.7,
  author =	{Fromont, Elisa and Quiniou, Ren\'{e} and Cordier, Marie-Odile},
  title =	{{Parallel universes to improve the diagnosis of cardiac arrhythmias}},
  booktitle =	{Parallel Universes and Local Patterns},
  pages =	{1--4},
  series =	{Dagstuhl Seminar Proceedings (DagSemProc)},
  ISSN =	{1862-4405},
  year =	{2007},
  volume =	{7181},
  editor =	{Michael R. Berthold and Katharina Morik and Arno Siebes},
  publisher =	{Schloss Dagstuhl -- Leibniz-Zentrum f{\"u}r Informatik},
  address =	{Dagstuhl, Germany},
  URL =		{https://drops.dagstuhl.de/entities/document/10.4230/DagSemProc.07181.7},
  URN =		{urn:nbn:de:0030-drops-12600},
  doi =		{10.4230/DagSemProc.07181.7},
  annote =	{Keywords: Parallel universes, inductive logic programming, medical application, declarative bias}
}
Document
DOI: 10.4230/DagSemProc.07181.8
Parallel Universes: Multi-Criteria Optimization

Authors: Claus Weihs and Heike Trautmann

Abstract
In this paper parallel universes are defined by their relation to multi-criteria optimization combined with an explicit or implicit link for the unambiguous identification of an optimum. As an explicit link function the desirability index is introduced. Desirabilities are also used for restricting the Pareto set to desired parts.
Cite as

Claus Weihs and Heike Trautmann. Parallel Universes: Multi-Criteria Optimization. In Parallel Universes and Local Patterns. Dagstuhl Seminar Proceedings, Volume 7181, pp. 1-10, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2007)

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@InProceedings{weihs_et_al:DagSemProc.07181.8,
  author =	{Weihs, Claus and Trautmann, Heike},
  title =	{{Parallel Universes: Multi-Criteria Optimization}},
  booktitle =	{Parallel Universes and Local Patterns},
  pages =	{1--10},
  series =	{Dagstuhl Seminar Proceedings (DagSemProc)},
  ISSN =	{1862-4405},
  year =	{2007},
  volume =	{7181},
  editor =	{Michael R. Berthold and Katharina Morik and Arno Siebes},
  publisher =	{Schloss Dagstuhl -- Leibniz-Zentrum f{\"u}r Informatik},
  address =	{Dagstuhl, Germany},
  URL =		{https://drops.dagstuhl.de/entities/document/10.4230/DagSemProc.07181.8},
  URN =		{urn:nbn:de:0030-drops-12557},
  doi =		{10.4230/DagSemProc.07181.8},
  annote =	{Keywords: Parallel Universes, Multi-Criteria Optimization, Pareto Optimization, Desirability Index, Desirability Functions}
}
Document
DOI: 10.4230/DagSemProc.07181.9
Reliably Capture Local Clusters in Noisy Domains From Parallel Universes

Authors: Frank Höppner and Mirko Böttcher

Abstract
When seeking for small local patterns it is very intricate to distinguish between incidental agglomeration of noisy points and true local patterns. We propose a new approach that addresses this problem by exploiting temporal information which is contained in most business data sets. The algorithm enables the detection of local patterns in noisy data sets more reliable compared to the case when the temporal information is ignored. This is achieved by making use of the fact that noise does not reproduce its incidental structure but even small patterns do. In particular, we developed a method to track clusters over time based on an optimal match of data partitions between time periods.
Cite as

Frank Höppner and Mirko Böttcher. Reliably Capture Local Clusters in Noisy Domains From Parallel Universes. In Parallel Universes and Local Patterns. Dagstuhl Seminar Proceedings, Volume 7181, pp. 1-2, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2007)

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@InProceedings{hoppner_et_al:DagSemProc.07181.9,
  author =	{H\"{o}ppner, Frank and B\"{o}ttcher, Mirko},
  title =	{{Reliably Capture Local Clusters in Noisy Domains From Parallel Universes}},
  booktitle =	{Parallel Universes and Local Patterns},
  pages =	{1--2},
  series =	{Dagstuhl Seminar Proceedings (DagSemProc)},
  ISSN =	{1862-4405},
  year =	{2007},
  volume =	{7181},
  editor =	{Michael R. Berthold and Katharina Morik and Arno Siebes},
  publisher =	{Schloss Dagstuhl -- Leibniz-Zentrum f{\"u}r Informatik},
  address =	{Dagstuhl, Germany},
  URL =		{https://drops.dagstuhl.de/entities/document/10.4230/DagSemProc.07181.9},
  URN =		{urn:nbn:de:0030-drops-12617},
  doi =		{10.4230/DagSemProc.07181.9},
  annote =	{Keywords: Local pattern, time, parallel universe}
}
Document
DOI: 10.4230/DagSemProc.07181.10
Subspace outlier mining in large multimedia databases

Authors: Ira Assent, Ralph Krieger, Emmanuel Müller, and Thomas Seidl

Abstract
Increasingly large multimedia databases in life sciences, e-commerce, or monitoring applications cannot be browsed manually, but require automatic knowledge discovery in databases (KDD) techniques to detect novel and interesting patterns. One of the major tasks in KDD, clustering, aims at grouping similar objects into clusters, separating dissimilar objects. Density-based clustering has been shown to detect arbitrarily shaped clusters even in noisy data bases. In high-dimensional data bases, meaningful clusters can no longer be detected due to the "curse of dimensionality". Consequently, subspace clustering searches for clusters hidden in any subset of the set of dimensions. As the number of subspaces is exponential in the number of dimensions, traditional approaches use fixed pruning thresholds. This results in dimensionality bias, i.e. with growing dimensionality, more clusters are missed. Clustering information is very useful for applications like fraud detection where outliers, i.e. objects which differ from all clusters, are searched. In subspace clustering, an object may be an outlier with respect to some groups, but not with respect to others, leading to possibly conflicting information. We propose a density-based unbiased subspace clustering model for outlier detection. We define outliers with respect to all maximal and non-redundant subspace clusters, taking their distance (deviation in attribute values), relevance (number of attributes covered) and support (number of objects covered) into account. We demonstrate the quality of our subspace clustering results in experiments on real world and synthetic databases and discuss our outlier model.
Cite as

Ira Assent, Ralph Krieger, Emmanuel Müller, and Thomas Seidl. Subspace outlier mining in large multimedia databases. In Parallel Universes and Local Patterns. Dagstuhl Seminar Proceedings, Volume 7181, pp. 1-8, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2007)

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@InProceedings{assent_et_al:DagSemProc.07181.10,
  author =	{Assent, Ira and Krieger, Ralph and M\"{u}ller, Emmanuel and Seidl, Thomas},
  title =	{{Subspace outlier mining in large multimedia databases}},
  booktitle =	{Parallel Universes and Local Patterns},
  pages =	{1--8},
  series =	{Dagstuhl Seminar Proceedings (DagSemProc)},
  ISSN =	{1862-4405},
  year =	{2007},
  volume =	{7181},
  editor =	{Michael R. Berthold and Katharina Morik and Arno Siebes},
  publisher =	{Schloss Dagstuhl -- Leibniz-Zentrum f{\"u}r Informatik},
  address =	{Dagstuhl, Germany},
  URL =		{https://drops.dagstuhl.de/entities/document/10.4230/DagSemProc.07181.10},
  URN =		{urn:nbn:de:0030-drops-12574},
  doi =		{10.4230/DagSemProc.07181.10},
  annote =	{Keywords: Data mining, outlier detection, subspace clustering, density-based clustering}
}

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  • Authors
  • A
  • Adams, Niall
  • Assent, Ira
  • B
  • Berthold, Michael R.
  • Böttcher, Mirko
  • C
  • Cordier, Marie-Odile
  • Crémilleux, Bruno
  • F
  • Fromont, Elisa
  • H
  • Hand, David J.
  • Höppner, Frank
  • K
  • Krieger, Ralph
  • M
  • Morik, Katharina
  • Müller, Emmanuel
  • Q
  • Quiniou, René
  • S
  • Seidl, Thomas
  • Siebes, Arno
  • Soulet, Arnaud
  • T
  • Trautmann, Heike
  • W
  • Weihs, Claus
  • Wiswedel, Bernd
  • Wurst, Michael

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