Treffer: Fuzzy clustering in parallel universes

Title:
Fuzzy clustering in parallel universes
Authors:
WISWEDEL, Bernd, BERTHOLD, Michael R
Source:
North American Fuzzy Information Processing Society Annual Conference NAFIPS’2005, June 22-25, Ann Arbor, MIInternational journal of approximate reasoning. 45(3):439-454
Publisher Information:
Amsterdam: Elsevier, 2007.
Publication Year:
2007
Physical Description:
print, 19 ref
Original Material:
INIST-CNRS
Subject Terms:
Computer science, Informatique, Sciences exactes et technologie, Exact sciences and technology, Sciences appliquees, Applied sciences, Informatique; automatique theorique; systemes, Computer science; control theory; systems, Logiciel, Software, Systèmes informatiques et systèmes répartis. Interface utilisateur, Computer systems and distributed systems. User interface, Intelligence artificielle, Artificial intelligence, Apprentissage et systèmes adaptatifs, Learning and adaptive systems, Algorithme flou, Fuzzy algorithm, Algoritmo borroso, Algorithme réparti, Distributed algorithm, Algoritmo repartido, Analyse amas, Cluster analysis, Analisis cluster, Analyse donnée, Data analysis, Análisis datos, Bioinformatique, Bioinformatics, Bioinformática, Classification, Clasificación, Fonction objectif, Objective function, Función objetivo, Intelligence artificielle, Artificial intelligence, Inteligencia artificial, Logique floue, Fuzzy logic, Lógica difusa, Modélisation, Modeling, Modelización, Méthode biologique, Biological method, Método biológico, Système réparti, Distributed system, Sistema repartido, Fuzzy clustering, Multiple descriptor spaces, Noise handling, Parallel universes
Document Type:
Konferenz Conference Paper
File Description:
text
Language:
English
Author Affiliations:
ALTANA-Chair for Bioinformatics and Information Mining, Department of Computer and Information Science. University of Konstanz, 78457 Konstanz, Germany
ISSN:
0888-613X
Access URL:
http://pascal-francis.inist.fr/vibad/index.php?action=search&terms=18997623
Rights:
Copyright 2007 INIST-CNRS
CC BY 4.0
Sauf mention contraire ci-dessus, le contenu de cette notice bibliographique peut être utilisé dans le cadre d’une licence CC BY 4.0 Inist-CNRS / Unless otherwise stated above, the content of this bibliographic record may be used under a CC BY 4.0 licence by Inist-CNRS / A menos que se haya señalado antes, el contenido de este registro bibliográfico puede ser utilizado al amparo de una licencia CC BY 4.0 Inist-CNRS
Notes:
Computer science; theoretical automation; systems
Accession Number:
edscal.18997623
Database:
PASCAL Archive

Weitere Informationen

We present an extension of the fuzzy c-Means algorithm, which operates simultaneously on different feature spaces-so-called parallel universes-and also incorporates noise detection. The method assigns membership values of patterns to different universes, which are then adopted throughout the training. This leads to better clustering results since patterns not contributing to clustering in a universe are (completely or partially) ignored. The method also uses an auxiliary universe to capture patterns that do not contribute to any of the clusters in the real universes and therefore are likely to represent noise. The outcome of the algorithm is clusters distributed over different parallel universes, each modeling a particular, potentially overlapping subset of the data and a set of patterns detected as noise. One potential target application of the proposed method is biological data analysis where different descriptors for molecules are available but none of them by itself shows global satisfactory prediction results.