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
fDate :
July 31 2011-Aug. 5 2011
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;
Conference_Titel :
Neural Networks (IJCNN), The 2011 International Joint Conference on
Conference_Location :
San Jose, CA
Print_ISBN :
978-1-4244-9635-8
DOI :
10.1109/IJCNN.2011.6033433
