DocumentCode
3496605
Title
A semi-supervised clustering algorithm that integrates heterogeneous dissimilarities and data sources
Author
Martín-Merino, Manuel
Author_Institution
Dept. of Comput. Sci., Univ. Pontificia of Salamanca, Salamanca, Spain
fYear
2011
fDate
July 31 2011-Aug. 5 2011
Firstpage
1732
Lastpage
1739
Abstract
Clustering algorithms depend strongly on the dissimilarity considered to evaluate the sample proximities. In real applications, several dissimilarities are available that may come from different object representations or data sources. Each dissimilarity provides usually complementary information about the problem. Therefore, they should be integrated in order to reflect accurately the object proximities.
Keywords
learning (artificial intelligence); pattern clustering; data source; heterogeneous dissimilarity; learning algorithm; object proximity; object representation; semisupervised clustering algorithm; Clustering algorithms; Euclidean distance; Gene expression; Kernel; Optimization; Support vector machines;
fLanguage
English
Publisher
ieee
Conference_Titel
Neural Networks (IJCNN), The 2011 International Joint Conference on
Conference_Location
San Jose, CA
ISSN
2161-4393
Print_ISBN
978-1-4244-9635-8
Type
conf
DOI
10.1109/IJCNN.2011.6033433
Filename
6033433
Link To Document